Off-the-Shelf Datasets
Our licensable datasets to jumpstart your AI projects

Product Catalog
While open data or public datasets are convenient, we offer an extensive catalog of ‘off-the-shelf’, 250+ licensable datasets across 80 languages across multiple dialects for a variety of common AI use cases. We are excited to announce 30+ new datasets for 2020 that deliver immediate value to our customers. Among our offerings, you will find datasets for speech recognition, learning datasets for machine learning algorithms, all created with the most advanced available data science.

Speed
Available immediately to support your AI/ML projects today

Cost Effective
Licensed datasets are more economical than custom data collection

Expertise
20+ years’ data collection experience

Support All Data Types
Image, video, speech, audio, and text

Scale
Provide the right amount of data to train your models effectively

Quality
Improve quality and minimize bias in your AI models
Dataset Name | Product Type | Common Use Cases | Recording Device | Unit |
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- Product Type
- Language
- Common Use Case
- ASR
- Action Classification
- Automatic Captioning
- Baby Monitor
- Call Centre
- Chatbot
- Content Classification
- Conversational AI
- Document Processing
- Document Search
- Facial Recognition
- Fitness Applications
- Gesture Recognition
- In Car HMI & Entertainment
- Keyword Spotting
- Language Modelling
- MT
- NER
- Search Engines
- Security & Other Consumer Applications
- Speech Analytics
- TTS
- Virtual Assistant
Reset Filter
Dataset Name | Product Type | Common Use Cases | Recording Device | Unit | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
138 | Text | ASR, TTS, Language Modelling | N/A | 12,000 words | Add Quote | sqi_ALB_PHON | Appen Global | Pronunciation Dictionary | Albanian | Albania | N/A | N/A | N/A | N/A | 12,000 | N/A | text | Albanian (Albania) Pronunciation Dictionary | ||
139 | Text | ASR, TTS, Language Modelling | N/A | 45,000 words | Add Quote | amh_ETH_PHON | Appen Global | Pronunciation Dictionary | Amharic | Ethiopia | N/A | N/A | N/A | N/A | 45,000 | N/A | text | Amharic (Ethiopia) Pronunciation Dictionary | ||
144 | Text | ASR, TTS, Language Modelling | N/A | 11,000 words | Add Quote | ara_DZA_PHON | Appen Global | Pronunciation Dictionary | Arabic | Algeria | N/A | N/A | N/A | N/A | 11,000 | N/A | text | Arabic (Algeria) Pronunciation Dictionary | ||
20 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 29 hours | Add Quote | EAR_ASR001 | Appen Global | Conversational Speech | Arabic | Algeria | Low background noise (home/office) | 496 | 2 | Available on request | 11,327 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words For the majority of calls, both speakers (in-line/out-line) were collected and transcribed however, for a smaller number of calls, only one half of the conversation was collected and transcribed | Arabic (Eastern Algeria) conversational telephony | |
140 | Text | ASR, TTS, Language Modelling | N/A | 40,000 words | Add Quote | ara_EGY_PHON | Appen Global | Pronunciation Dictionary | Arabic | Egypt | N/A | N/A | N/A | N/A | 40,000 | N/A | text | Arabic (Egypt) Pronunciation Dictionary | ||
114 | Audio | ASR, Virtual Assistant, Chatbot | Mobile phone | 352 hours | Add Quote | ARE_ASR001_CN | Appen China | Scripted Speech | Arabic | Egypt | Low background noise (home/office) | 627 | 1 | 128,908 | 207,576 | 16 | wav | Dataset is fully transcribed | Arabic (Egypt) scripted smartphone | |
142 | Text | ASR, TTS, Language Modelling | N/A | 13,000 words | Add Quote | ara_IRQ_POS | Appen Global | Part of Speech Dictionary | Arabic | Iraq | N/A | N/A | N/A | N/A | 13,000 | N/A | text | Arabic (Iraq) Part of Speech Dictionary | ||
141 | Text | ASR, TTS, Language Modelling | N/A | 15,000 words | Add Quote | ara_IRQ_PHON | Appen Global | Pronunciation Dictionary | Arabic | Iraq | N/A | N/A | N/A | N/A | 15,000 | N/A | text | Person names | Arabic (Iraq) Pronunciation Dictionary | |
143 | Text | ASR, TTS, Language Modelling | N/A | 48,000 words | Add Quote | ara_LBY_PHON | Appen Global | Pronunciation Dictionary | Arabic | Libya | N/A | N/A | N/A | N/A | 48,000 | N/A | text | Arabic (Libya) Pronunciation Dictionary | ||
65 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 12 hours | Add Quote | MSA_ASR001 | Global Phone | Scripted Speech | Arabic | Tunisia | Low background noise (home/office) | 78 | 1 | 4,908 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | Arabic (Modern Standard Arabic) scripted microphone | |
112 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 33 hours | Add Quote | ARY_ASR001 | Appen Global | Conversational Speech | Arabic | Morocco | Low background noise | 180 | 2 | 80,544 | 23,836 | 8 | alaw | Each speaker participated in 1 to 4 conversations. Speakers are identified by a unique 4-digit speaker ID which is recorded in the demographic file Transcription is available in original script and fully reversible Romanised version with accompanying pronunciation lexicon English translation of product transcription is available (ARY_MT001, ARY_ASRMT001) | Arabic (Morocco) conversational telephony | |
113 | Text | MT, Chatbot , Conversational AI | N/A | 80,544 utterances | Add Quote | ARY_MT001 | Appen Global | Conversational Translation | Arabic | Morocco | N/A | 180 | N/A | 80,430 | 23,844 | N/A | text | Corresponding audio, transcription, fully reversible romanised transcription and pronunciation lexicon data are available (ARY_ASR001, ARY_ASRMT001) | Arabic (Morocco) conversational telephony translation | |
146 | Text | ASR, TTS, Language Modelling | N/A | 60,000 words | Add Quote | ara_MAR_PHON | Appen Global | Pronunciation Dictionary | Arabic | Morocco | N/A | N/A | N/A | N/A | 60,000 | N/A | text | Arabic (Morocco) Pronunciation Dictionary | ||
147 | Text | ASR, TTS, Language Modelling | N/A | 40,000 words | Add Quote | arb_N/A_PHON | Appen Global | Pronunciation Dictionary | Arabic | N/A | N/A | N/A | N/A | N/A | 40,000 | N/A | text | Arabic (N/A) Pronunciation Dictionary | ||
115 | Audio | ASR, Virtual Assistant, Chatbot | Mobile phone | 322 hours | Add Quote | ARS_ASR001_CN | Appen China | Scripted Speech | Arabic | Saudi Arabia | Low background noise (home/office) | 227 | 1 | 104,574 | 156,282 | 16 | wav | Dataset is fully transcribed | Arabic (Saudi Arabia) scripted smartphone | |
149 | Text | ASR, TTS, Language Modelling | N/A | 17,000 words | Add Quote | ara_SDN_PHON | Appen Global | Pronunciation Dictionary | Arabic | Sudan | N/A | N/A | N/A | N/A | 17,000 | N/A | text | Arabic (Sudan) Pronunciation Dictionary | ||
148 | Text | ASR, TTS, Language Modelling | N/A | 75,000 words | Add Quote | ara_ARE_PHON | Appen Global | Pronunciation Dictionary | Arabic | United Arab Emirates | N/A | N/A | N/A | N/A | 75,000 | N/A | text | Arabic (United Arab Emirates) Pronunciation Dictionary | ||
122 | Audio | ASR, Virtual Assistant, Chatbot | Mobile phone | 170 hours | Add Quote | ARU_ASR001_CN | Appen China | Scripted Speech | Arabic | United Arab Emirates | Low background noise (home/office) | 133 | 1 | 42,352 | 85,775 | 16 | wav | Dataset is fully transcribed | Arabic (United Arab Emirates) scripted smartphone | |
70 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone and landline | 48 hours | Add Quote | OrienTel United Arab Emirates MCA (Modern Colloquial Arabic) | Nuance | Scripted Speech | Arabic | United Arab Emirates | Low background noise | 880 | 1 | 43,000 | Available on request | 8 | alaw | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 49 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items, phonetically rich sentences and words and spontaneous items for control | Arabic (United Arab Emirates) scripted telephony | |
71 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone and landline | 31 hours | Add Quote | OrienTel United Arab Emirates MSA (Modern Standard Arabic) | Nuance | Scripted Speech | Arabic | United Arab Emirates | Low background noise | 500 | 1 | 24,500 | Available on request | 8 | alaw | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 49 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items, phonetically rich sentences and words and spontaneous items for control | Arabic (United Arab Emirates) scripted telephony | |
9 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 86 hours | Add Quote | CGA_ASR001 | Appen Global | Scripted Speech | Arabic | United Arab Emirates; Saudi Arabia | Low background noise (home/office) | 150 | 4 | 42,000 | 19,245 | 16 | alaw | Complete transcriptions of the content of the speech files at a word level All acoustic events have been tagged using conventions derived from the SpeechDATmodel All transcriptions fully vowelized 280 prompts per speaker including 30 Person names (first name and family name) from a set of 15, 10 single isolated digits 0-10, 8-digit sequences (randomly generated), 200 phonetically balanced sentences, 30 x 10-word phonetically balanced word strings | Arabic (United Arab Emirates/ Saudi Arabia) scripted microphone | |
130 | Text | NER, Content Classification, Search Engines | N/A | 20,774 sentences | Add Quote | ARB_NER001 | Appen Global | News NER | Standard Arabic | N/A | N/A | N/A | N/A | 20,774 | Available on request | N/A | text | Arabic NER news text | ||
150 | Text | ASR, TTS, Language Modelling | N/A | 40,000 words | Add Quote | asm_IND_PHON | Appen Global | Pronunciation Dictionary | Assamese | India | N/A | N/A | N/A | N/A | 40,000 | N/A | text | Assamese (India) Pronunciation Dictionary | ||
124 | Audio | Baby Monitor, Security & Other Consumer Applications | Mobile phone | 3 hours | Add Quote | CRY_ASR001 | Appen China | Human Sound | N/A | China | Low background noise (home/office) | 100 | 1 | NA | NA | 16 | wav | Crying sound of babies 0-3 years old, each lasting around 2 minutes. | Baby crying audio | |
4 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 31 hours | Add Quote | BAH_ASR001 | Appen Global | Conversational Speech | Indonesian | Indonesia | Low background noise | 1,002 | 2 | Available on request | 11,480 | 8 | wav | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words For a large proportion of calls, only one half of the conversation was collected and transcribed | Bahasa Indonesia conversational telephony | |
153 | Text | ASR, TTS, Language Modelling | N/A | 10,000 words | Add Quote | eus_ESP_PHON | Appen Global | Pronunciation Dictionary | Basque | Spain | N/A | N/A | N/A | N/A | 10,000 | N/A | text | Basque (Spain) Pronunciation Dictionary | ||
6 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 47 hours | Add Quote | BEN_ASR001 | Appen Global | Conversational Speech | Bengali | Bangladesh | Mixed (in-car, roadside, home/office) | 1,000 | 2 | Available on request | 17,922 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words | Bengali (Bangladesh) conversational telephony | |
154 | Text | ASR, TTS, Language Modelling | N/A | 29,000 words | Add Quote | ben_IND_PHON | Appen Global | Pronunciation Dictionary | Bengali | India | N/A | N/A | N/A | N/A | 29,000 | N/A | text | Bengali (India) Pronunciation Dictionary | ||
7 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 38 hours | Add Quote | BUL_ASR001 | Appen Global | Conversational Speech | Bulgarian | Bulgaria | Low background noise (home/office) | 217 | 2 | Available on request | 22,342 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words 200 telephony conversations are recorded for this project - 100 speakers make 2 calls each (1 from landline, 1 from mobile) to a pool of 100 call receivers | Bulgarian (Bulgaria) conversational telephony | |
155 | Text | ASR, TTS, Language Modelling | N/A | 55,000 words | Add Quote | bul_BGR_PHON | Appen Global | Pronunciation Dictionary | Bulgarian | Bulgaria | N/A | N/A | N/A | N/A | 55,000 | N/A | text | Bulgarian (Bulgaria) Pronunciation Dictionary | ||
111 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 22 hours | Add Quote | BUL_ASR002 | Global Phone | Scripted Speech | Bulgarian | Bulgaria | Low background noise (home/office) | 77 | 1 | 8,674 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | Bulgarian (Bulgaria) scripted microphone | |
158 | Text | ASR, TTS, Language Modelling | N/A | 10,000 words | Add Quote | yue_HKG_POS | Appen Global | Part of Speech Dictionary | Cantonese | China | N/A | N/A | N/A | N/A | 10,000 | N/A | text | Traditional | Cantonese (China) Part of Speech Dictionary | |
156 | Text | ASR, TTS, Language Modelling | N/A | 37,000 words | Add Quote | yue_CHN_PHON | Appen Global | Pronunciation Dictionary | Cantonese | China | N/A | N/A | N/A | N/A | 37,000 | N/A | text | Simplified | Cantonese (China) Pronunciation Dictionary | |
157 | Text | ASR, TTS, Language Modelling | N/A | 40,000 words | Add Quote | yue_CHN_PHON | Appen Global | Pronunciation Dictionary | Cantonese | China | N/A | N/A | N/A | N/A | 40,000 | N/A | text | Traditional | Cantonese (China) Pronunciation Dictionary | |
159 | Text | ASR, TTS, Language Modelling | N/A | 10,000 words | Add Quote | cat_ESP_PHON | Appen Global | Pronunciation Dictionary | Catalan | Spain | N/A | N/A | N/A | N/A | 10,000 | N/A | text | Catalan (Spain) Pronunciation Dictionary | ||
160 | Text | ASR, TTS, Language Modelling | N/A | 20,000 words | Add Quote | ceb_PHL_PHON | Appen Global | Pronunciation Dictionary | Cebuano | Philippines | N/A | N/A | N/A | N/A | 20,000 | N/A | text | Cebuano (Philippines) Pronunciation Dictionary | ||
10 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 39 hours | Add Quote | CRO_ASR001 | Appen Global | Conversational Speech | Croatian | Croatia | Low background noise (home/office) | 200 | 2 | Available on request | 23,919 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words 200 telephony conversations are recorded for this project - 100 speakers make 2 calls each (1 from landline, 1 from mobile) to a pool of 100 call receivers | Croatian (Croatia) conversational telephony | |
161 | Text | ASR, TTS, Language Modelling | N/A | 20,000 words | Add Quote | hrv_HRV_PHON | Appen Global | Pronunciation Dictionary | Croatian | Croatia | N/A | N/A | N/A | N/A | 20,000 | N/A | text | Croatian (Croatia) Pronunciation Dictionary | ||
11 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 11 hours | Add Quote | CRO_ASR002 | Global Phone | Scripted Speech | Croatian | Croatia | Low background noise (home/office) | 94 | 1 | 4,499 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | Croatian (Croatia) scripted microphone | |
116 | Audio | ASR, Virtual Assistant, Chatbot | Mobile phone | 263 hours | Add Quote | CRO_ASR003_CN | Appen China | Scripted Speech | Croatian | Croatia | Low background noise (home/office) | 243 | 1 | 73,467 | 136,140 | 16 | wav | Dataset is fully transcribed | Croatian (Croatia) scripted smartphone | |
162 | Text | ASR, TTS, Language Modelling | N/A | 50,000 words | Add Quote | ces_CZE_PHON | Appen Global | Pronunciation Dictionary | Czech | Czech Republic | N/A | N/A | N/A | N/A | 50,000 | N/A | text | Czech (Czech Republic) Pronunciation Dictionary | ||
12 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 31 hours | Add Quote | CZE_ASR001 | Global Phone | Scripted Speech | Czech | Czech Republic | Low background noise (home/office) | 102 | 1 | 12,425 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | Czech (Czech Republic) scripted microphone | |
13 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 93 hours | Add Quote | Czech SpeechDat(E) Dataset | Nuance | Scripted Speech | Czech | Czech Republic | Low background noise | 1,000 | 1 | 52,000 | Available on request | 8 | alaw | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 52 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items, and phonetically rich words and sentences | Czech (Czech Republic) scripted telephony | |
164 | Text | ASR, TTS, Language Modelling | N/A | 100,000 words | Add Quote | dan_DNK_POS | Appen Global | Part of Speech Dictionary | Danish | Denmark | N/A | N/A | N/A | N/A | 100,000 | N/A | text | Danish (Denmark) Part of Speech Dictionary | ||
163 | Text | ASR, TTS, Language Modelling | N/A | 107,000 words | Add Quote | dan_DNK_PHON | Appen Global | Pronunciation Dictionary | Danish | Denmark | N/A | N/A | N/A | N/A | 107,000 | N/A | text | Danish (Denmark) Pronunciation Dictionary | ||
90 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 53 hours | Add Quote | Speecon Danish | Nuance | Scripted Speech | Danish | Denmark | Mixed (office, entertainment, car, public place) | 600 (550 adult speakers and 50 child speakers) | 4 | 170,000 | Available on request | 16 | alaw | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 290 prompts per adult speaker and 210 prompts per child speaker including digits, natural numbers, letter strings, personal, place and business names, application words for adult speakers, command (toy, phone and general) for child speakers, phonetically rich words and sentences and free and elicited spontaneous responses for adult speakers | Danish (Denmark) scripted microphone | |
15 | Audio | ASR, Automatic Captioning, Keyword Spotting | Microphone | 51 hours | Add Quote | DAR_BRC001 | Appen Global | Broadcast Speech | Dari | Afghanistan | Low background noise (studio) | N/A | 1 | Available on request | Available on request | N/A | wav | Dataset is fully transcribed and timestamped Dataset is largely speech only and does not include music or advertisements Data types include: talk shows, interviews, news broadcasts (excluding news reading by anchors) | Dari (Afghanistan) broadcast data | |
14 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 40 hours | Add Quote | DAR_ASR001 | Appen Global | Conversational Speech | Dari | Afghanistan | Low background noise | 500 | 2 | Available on request | 11,168 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is largely speech only and does not include music or advertisements | Dari (Afghanistan) conversational telephony | |
165 | Text | ASR, TTS, Language Modelling | N/A | 30,000 words | Add Quote | prs_AFG_PHON | Appen Global | Pronunciation Dictionary | Dari | Afghanistan | N/A | N/A | N/A | N/A | 30,000 | N/A | text | Dari (Afghanistan) Pronunciation Dictionary | ||
166 | Text | ASR, TTS, Language Modelling | N/A | 20,000 words | Add Quote | luo_KEN_PHON | Appen Global | Pronunciation Dictionary | Dholuo | Kenya | N/A | N/A | N/A | N/A | 20,000 | N/A | text | Dholuo (Kenya) Pronunciation Dictionary | ||
91 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 47 hours | Add Quote | Speecon Dutch from Belgium | Nuance | Scripted Speech | Dutch | Belgium | Mixed (office, entertainment, car, public place) | 600 (550 adult speakers and 50 child speakers) | 4 | 170,000 | Available on request | 16 | alaw | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 290 prompts per adult speaker and 210 prompts per child speaker including digits, natural numbers, letter strings, personal, place and business names, application words for adult speakers, command (toy, phone and general) for child speakers, phonetically rich words and sentences and free and elicited spontaneous responses for adult speakers | Dutch (Belgium) scripted microphone | |
33 | Audio | ASR, Call Centre, Virtual Assistant | Microphone | 80 hours | Add Quote | Flemish SpeechDat(II) FDB-1000 (FIXED1FL) | Nuance | Scripted Speech | Dutch | Belgium | Low background noise | 1,000 | 1 | 52,000 | Available on request | 8 | alaw | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 52 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items, phonetically rich sentences and words and spontaneous items for control | Dutch (Belgium) scripted telephony | |
19 | Audio | ASR, Virtual Assistant, In Car HMI & Entertainment | Microphone and mobile phone | 27 hours | Add Quote | Dutch and Flemish SpeechDat-Car | Nuance | Scripted Speech | Dutch | Netherland; Belgium | Mixed (in-car) | 302 | 5 | 15,100 | Available on request | 16 and 8 | alaw | Dataset is fully transcribed and is accompanied by a pronunciation lexicon and validation report 125 prompts per adult speaker including digits, natural numbers, letter strings, personal, place and business names (some spontaneous), generic command and control items, phonetically rich words and sentences and prompts for spontaneous speech | Dutch (Netherlands & Belgium) scripted in-car | |
66 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 36 hours | Add Quote | NLD_ASR001 | Appen Global | Conversational Speech | Dutch | Netherlands | Low background noise | 200 | 2 | Available on request | 14,964 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words 200 telephony conversations are recorded for this project - 100 speakers make 2 calls each (1 from landline, 1 from mobile) to a pool of 100 call receivers | Dutch (Netherlands) conversational telephony | |
167 | Text | ASR, TTS, Language Modelling | N/A | 45,000 words | Add Quote | nld_NLD_PHON | Appen Global | Pronunciation Dictionary | Dutch | Netherlands | N/A | N/A | N/A | N/A | 45,000 | N/A | text | Dutch (Netherlands) Pronunciation Dictionary | ||
92 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 68 hours | Add Quote | Speecon Dutch from the Netherlands | Nuance | Scripted Speech | Dutch | Netherlands | Mixed (office, entertainment, car, public place) | 600 (550 adult speakers and 50 child speakers) | 4 | 170,000 | Available on request | 16 | alaw | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 290 prompts per adult speaker and 210 prompts per child speaker including digits, natural numbers, letter strings, personal, place and business names, application words for adult speakers, command (toy, phone and general) for child speakers, phonetically rich words and sentences and free and elicited spontaneous responses for adult speakers | Dutch (Netherlands) scripted microphone | |
125 | Image | Facial Recognition | Camera | 13500 images | Add Quote | IMG_FACE_KEN_CN | Appen China | Human Face | N/A | Kenya | Mixed background and lighting conditions | 100 | NA | NA | NA | NA | jpg | East African facial images | ||
21 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 28 hours | Add Quote | ENA_ASR001 | Appen Global | Conversational Speech | English | Egypt | Low background noise | 250 | 2 | Available on request | 5,619 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words Average length of calls: 10-15 mins | English (Arabic - Levant/Egypt) conversational telephony | |
169 | Text | ASR, TTS, Language Modelling | N/A | 157,000 words | Add Quote | eng_AUS_PHON | Appen Global | Pronunciation Dictionary | English | Australia | N/A | N/A | N/A | N/A | 157,000 | N/A | text | English (Australia) Pronunciation Dictionary | ||
2 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone and landline | 92 hours | Add Quote | AUS_ASR001 | Appen Global | Scripted Speech | English | Australia | Low background noise (home/office) | 500 | 1 | 82,500 | 35,137 | 8 | alaw | Fully transcribed to SpeechDAT type conventions Dataset is accompanied by a pronunciation lexicon containing all transcribed words 162 prompts (read speech) per speaker including digits, natural numbers, letter strings, personal, place, and business names, confirmation items (yes, no + fuzzy), generic command and control items (from a set of 215), phonetically rich sentences and words | English (Australia) scripted telephony | |
3 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone and landline | 118 hours | Add Quote | AUS_ASR002 | Appen Global | Scripted Speech | English | Australia | Mixed | 1,000 | 1 | 75,000 | 19 | 8 | alaw | Fully transcribed to SpeechDAT type conventions Dataset is accompanied by a pronunciation lexicon containing all transcribed words 75 prompts per speaker including digits, natural numbers, letter strings, personal, place, and business names, confirmation items (yes, no + fuzzy), generic command and control items, phonetically rich sentences and words The prompts are a mixture of 'read' and 'elicited' items where 5 prompts per script are 'spontaneous free speech' | English (Australia) scripted telephony | |
171 | Text | ASR, TTS, Language Modelling | N/A | 3,000 words | Add Quote | eng_CAN_POS | Appen Global | Part of Speech Dictionary | English | Canada | N/A | N/A | N/A | N/A | 3,000 | N/A | text | English (Canada) Part of Speech Dictionary | ||
170 | Text | ASR, TTS, Language Modelling | N/A | 50,000 words | Add Quote | eng_CAN_PHON | Appen Global | Pronunciation Dictionary | English | Canada | N/A | N/A | N/A | N/A | 50,000 | N/A | text | English (Canada) Pronunciation Dictionary | ||
22 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone and landline | 144 hours | Add Quote | ENC_ASR001 | Appen Global | Scripted Speech | English | Canada | Mixed | 1,000 | 1 | 99,000 | 12,483 | 8 | alaw or wav | Fully transcribed to SALA II/SpeechDAT type conventions Dataset is accompanied by a pronunciation lexicon containing all transcribed words 99 prompts per speaker including digits, natural numbers, letter strings, personal, place, and business names, confirmation items (yes, no + fuzzy), generic command and control items, phonetically rich sentences and words | English (Canada) scripted telephony | |
173 | Text | ASR, TTS, Language Modelling | N/A | 18,000 words | Add Quote | eng_HKG_PHON | Appen Global | Pronunciation Dictionary | English | Hong Kong | N/A | N/A | N/A | N/A | 18,000 | N/A | text | English (Hong Kong) Pronunciation Dictionary | ||
25 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 67 hours | Add Quote | ENI_ASR002 | Appen Global | Conversational Speech | English | India | Low background noise | 540 | 2 | 77,565 | 11,646 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words 271 telephony conversations are recorded for this project | English (India) conversational telephony | |
175 | Text | ASR, TTS, Language Modelling | N/A | 13,000 words | Add Quote | eng_IND_POS | Appen Global | Part of Speech Dictionary | English | India | N/A | N/A | N/A | N/A | 13,000 | N/A | text | English (India) Part of Speech Dictionary | ||
174 | Text | ASR, TTS, Language Modelling | N/A | 60,000 words | Add Quote | eng_IND_PHON | Appen Global | Pronunciation Dictionary | English | India | N/A | N/A | N/A | N/A | 60,000 | N/A | text | English (India) Pronunciation Dictionary | ||
24 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone and landline | 217 hours | Add Quote | ENI_ASR001 | Appen Global | Scripted Speech | English | India | Mixed | 2,358 | 1 | 117,900 | 9,190 | 8 | alaw | Fully transcribed to SpeechDAT type conventions. Dataset is accompanied by a pronunciation lexicon [SAMPA] containing all transcribed words 49 prompts per speaker including digits, natural numbers, letter strings, personal, place, and business names, confirmation items (yes, no + fuzzy), generic command and control items, phonetically rich sentences and words | English (India) scripted telephony | |
176 | Text | ASR, TTS, Language Modelling | N/A | 12,000 words | Add Quote | eng_IRL_PHON | Appen Global | Pronunciation Dictionary | English | Ireland | N/A | N/A | N/A | N/A | 12,000 | N/A | text | English (Ireland) Pronunciation Dictionary | ||
177 | Text | ASR, TTS, Language Modelling | N/A | 50,000 words | Add Quote | eng_NZL_PHON | Appen Global | Pronunciation Dictionary | English | NZ | N/A | N/A | N/A | N/A | 50,000 | N/A | text | English (NZ) Pronunciation Dictionary | ||
23 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 53 hours | Add Quote | ENF_ASR001 | Appen Global | Conversational Speech | English | Philippines | Low background noise | 450 | 2 | 41,602 | 7,272 | 8 | alaw or wav | Dataset is fully transcribed and time stamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words Average length of calls: 10-15 mins | English (Philippines) conversational telephony | |
172 | Text | ASR, TTS, Language Modelling | N/A | 5,000 words | Add Quote | eng_PHL_PHON | Appen Global | Pronunciation Dictionary | English | Philippines | N/A | N/A | N/A | N/A | 5,000 | N/A | text | English (Philippines) Pronunciation Dictionary | ||
168 | Text | ASR, TTS, Language Modelling | N/A | 5,000 words | Add Quote | eng_ARE_PHON | Appen Global | Pronunciation Dictionary | English | United Arab Emirates | N/A | N/A | N/A | N/A | 5,000 | N/A | text | English (United Arab Emirates) Pronunciation Dictionary | ||
67 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone and landline | 33 hours | Add Quote | OrienTel English as spoken in the United Arab Emirates | Nuance | Scripted Speech | English | United Arab Emirates | Low background noise | 500 | 1 | 25,500 | Available on request | 8 | alaw | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 51 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items, phonetically rich sentences and words and spontaneous items for control | English (United Arab Emirates) scripted telephony | |
99 | Audio | TTS | Headset microphone | 10 hours | Add Quote | TC-STAR female baseline voice Laura | Nuance | Scripted Speech | English | United Kingdom | Low background noise (studio) | 1 | 1 | Available on request | Available on request | 96 | Available on request | Dataset includes manual orthographic transcription, automatic segmentation into phonemes, automatic generation of pitch marks (where a certain percentage of phonetic segments and pitch marks has been manually checked) Dataset is accompanied by a pronunciation lexicon with POS, lemma and phonetic transcription | English (United Kingdom) | |
100 | Audio | TTS | Headset microphone | 10 hours | Add Quote | TC-STAR male baseline voice Ian | Nuance | Scripted Speech | English | United Kingdom | Low background noise (studio) | 1 | 1 | Available on request | Available on request | 96 | Available on request | Dataset includes manual orthographic transcription, automatic segmentation into phonemes, automatic generation of pitch marks (where a certain percentage of phonetic segments and pitch marks has been manually checked) Dataset is accompanied by a pronunciation lexicon with POS, lemma and phonetic transcription | English (United Kingdom) | |
259 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 50 hours | Add Quote | UKE_ASR001B | Appen Global | Conversational Speech | English | United Kingdom | Low background noise | 1,150 | 2 | Available on request | 13,192 | 8 | wav | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words | English (United Kingdom) conversational telephony | |
104 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 150 hours | Add Quote | UKE_ASR001 | Appen Global | Conversational Speech | English | United Kingdom | Low background noise | 1,150 | 2 | 298,562 | 24,193 | 8 | wav | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words | English (United Kingdom) conversational telephony | |
179 | Text | ASR, TTS, Language Modelling | N/A | 155,000 words | Add Quote | eng_GBR_POS | Appen Global | Part of Speech Dictionary | English | United Kingdom | N/A | N/A | N/A | N/A | 155,000 | N/A | text | English (United Kingdom) Part of Speech Dictionary | ||
178 | Text | ASR, TTS, Language Modelling | N/A | 195,000 words | Add Quote | eng_GBR_PHON | Appen Global | Pronunciation Dictionary | English | United Kingdom | N/A | N/A | N/A | N/A | 195,000 | N/A | text | English (United Kingdom) Pronunciation Dictionary | ||
107 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone | 1000 hours | Add Quote | USE_ASR003 | Appen Global | Conversational Speech | English | United States | Low background noise | 2,000 | 1 | 500,000 | 52,586 | 16 | wav | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words Conversations cover a wide variety of topics including: study/major/work, hometown, living arrangements, weather and seasons, punctuality, TV programs/film) | English (United States) conversational smartphone | |
181 | Text | ASR, TTS, Language Modelling | N/A | 263,000 words | Add Quote | eng_USA_POS | Appen Global | Part of Speech Dictionary | English | United States | N/A | N/A | N/A | N/A | 263,000 | N/A | text | English (United States) Part of Speech Dictionary | ||
180 | Text | ASR, TTS, Language Modelling | N/A | 330,000 words | Add Quote | eng_USA_PHON | Appen Global | Pronunciation Dictionary | English | United States | N/A | N/A | N/A | N/A | 330,000 | N/A | text | English (United States) Pronunciation Dictionary | ||
93 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 53 hours | Add Quote | Speecon English (USA) database | Nuance | Scripted Speech | English | United States | Mixed (office, entertainment, car, public place) | 600 (550 adult speakers and 50 child speakers) | 4 | 170,000 | Available on request | 16 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 290 prompts per adult speaker and 210 prompts per child speaker including digits, natural numbers, letter strings, personal, place and business names, application words for adult speakers, command (toy, phone and general) for child speakers, phonetically rich words and sentences and free and elicited spontaneous responses for adult speakers | English (United States) scripted microphone | |
106 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 62 hours | Add Quote | USE_ASR001 | Appen Global | Scripted Speech | English | United States | Low background noise (studio) | 200 | 2 | 80,000 | 18,318 | 48 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words Each speaker read 400 prompts including digits, natural numbers, personal and city names, telephone numbers, generic command and control items, phonetically rich sentences and words | English (United States) scripted microphone | |
131 | Text | NER, Content Classification, Search Engines | N/A | 22,768 sentences | Add Quote | ENG_NER001 | Appen Global | News NER | English | N/A | N/A | N/A | N/A | 22,768 | Available on request | N/A | text | English NER news text | ||
32 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 30 hours | Add Quote | FAR_ASR002 | Appen Global | Conversational Speech | Iranian Persian | Iran | Mixed | 1,000 | 2 | Available on request | 12,358 | 8 | wav | Dataset is fully transcribed and time stamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words | Farsi/Persian (Iran) conversational telephony | |
31 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone and landline | 85 hours | Add Quote | FAR_ASR001 | Appen Global | Scripted Speech | Iranian Persian | Iran | Mixed | 789 | 1 | 38,400 | 8,716 | 8 | alaw | Fully transcribed to OrienTel type conventions Dataset is accompanied by a pronunciation lexicon [SAMPA] containing all transcribed words 48 prompts per speaker including digits, natural numbers, letter strings, personal, place, and business names, confirmation items (yes, no + fuzzy), generic command and control items, phonetically rich sentences and words | Farsi/Persian (Iran) scripted telephony | |
135 | Text | NER, Content Classification, Search Engines | N/A | 19,584 sentences | Add Quote | FAR_NER001 | Appen Global | News NER | Iranian Persian | Iran | N/A | N/A | N/A | 19,584 | Available on request | N/A | text | Farsi/Persian NER news text | ||
185 | Text | ASR, TTS, Language Modelling | N/A | 10,000 words | Add Quote | fin_FIN_POS | Appen Global | Part of Speech Dictionary | Finnish | Finland | N/A | N/A | N/A | N/A | 10,000 | N/A | text | Finnish (Finland) Part of Speech Dictionary | ||
128 | Image | Document Processing, Document Search | Camera | 7293 images | Add Quote | IMG_OCR_FIN_CN | Appen China | Document OCR | Finnish | Finland | Mixed lighting conditions | 4 | NA | NA | NA | NA | jpg | Images containing text, such as billboards / outer packaging / signage / magazines / menus, etc. | Finnish (Finland) printed text OCR | |
184 | Text | ASR, TTS, Language Modelling | N/A | 85,000 words | Add Quote | fin_FIN_PHON | Appen Global | Pronunciation Dictionary | Finnish | Finland | N/A | N/A | N/A | N/A | 85,000 | N/A | text | Finnish (Finland) Pronunciation Dictionary | ||
145 | Text | ASR, TTS, Language Modelling | N/A | 4,000 words | Add Quote | fra_DZA_PHON | Appen Global | Pronunciation Dictionary | French | Algeria | N/A | N/A | N/A | N/A | 4,000 | N/A | text | Arabic script | French (Algeria) Pronunciation Dictionary | |
5 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 76 hours | Add Quote | Belgian French SpeechDat(II) FDB-1000 (FIXED1BF) | Nuance | Scripted Speech | French | Belgium | Low background noise | 1,000 | 1 | 53,000 | Available on request | 8 | alaw | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 53 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items, phonetically rich sentences and words and spontaneous items for control | French (Belgium) scripted telephony | |
36 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 9 hours | Add Quote | FRC_ASR003 | Appen Global | Conversational Speech | French | Canada | Mixed | 68 | 2 | Available on request | 6,022 | 8 | alaw | Dataset is fully transcribed and time stamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words Average length of calls: 10-15 mins For the majority of calls, only one half of the conversation was collected and transcribed, however, for a smaller number of calls, both speakers (in-line/out-line) were collected and transcribed | French (Canada) conversational telephony | |
186 | Text | ASR, TTS, Language Modelling | N/A | 67,000 words | Add Quote | fra_CAN_PHON | Appen Global | Pronunciation Dictionary | French | Canada | N/A | N/A | N/A | N/A | 67,000 | N/A | text | French (Canada) Pronunciation Dictionary | ||
35 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 46 hours | Add Quote | FRC_ASR002 | Appen Global | Scripted Speech | French | Canada | Low background noise (home/office) | 150 | 1 | 22,500 | 10,755 | 16 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words 150 prompts per speaker including digits, digit strings (randomly generated), addressses and phonetically rich sentences and words | French (Canada) scripted microphone | |
34 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone | 131 hours | Add Quote | FRC_ASR001 | Appen Global | Scripted Speech | French | Canada | Mixed | 1,000 | 1 | 100,000 | 11,697 | 8 | alaw | Fully transcribed to SpeechDAT type conventions Dataset is accompanied by a pronunciation lexicon [SAMPA] containing all transcribed words 100 prompts per speaker including digits, natural numbers, letter strings, personal, place, and business names, confirmation items (yes, no + fuzzy), generic command and control items, phonetically rich sentences and words | French (Canada) scripted telephony | |
40 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 25 hours | Add Quote | FRF_ASR001 | Appen Global | Conversational Speech | French | France | Low background noise | 563 | 2 | Available on request | 11,922 | 8 | alaw | Dataset is fully transcribed and time stamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words For the majority of calls, both speakers (in-line/out-line) were collected and transcribed, however, for a smaller number of calls, only one half of the conversation was collected and transcribed | French (France) conversational telephony | |
39 | Audio | ASR, Virtual Assistant, In Car HMI & Entertainment | Microphone and mobile phone | Add Quote | French SpeechDat-Car | Nuance | Scripted Speech | French | France | Mixed (in-car) | 300 | 5 | 37,500 | Available on request | 16 and 8 | Available on request | Dataset is fully transcribed and is accompanied by a pronunciation lexicon and validation report Approximately 125 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names (some spontaneous), generic command and control items, phonetically rich words and sentences and prompts for spontaneous speech | French (France) In-Car | ||
188 | Text | ASR, TTS, Language Modelling | N/A | 95,000 words | Add Quote | fra_FRA_POS | Appen Global | Part of Speech Dictionary | French | France | N/A | N/A | N/A | N/A | 95,000 | N/A | text | French (France) Part of Speech Dictionary | ||
187 | Text | ASR, TTS, Language Modelling | N/A | 112,000 words | Add Quote | fra_FRA_PHON | Appen Global | Pronunciation Dictionary | French | France | N/A | N/A | N/A | N/A | 112,000 | N/A | text | French (France) Pronunciation Dictionary | ||
41 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 26 hours | Add Quote | FRF_ASR003 | Global Phone | Scripted Speech | French | France | Low background noise (home/office) | 98 | 1 | 10,273 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | French (France) scripted microphone | |
37 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 41 hours | Add Quote | French SpeechDat(II) FDB-1000 | Nuance | Scripted Speech | French | France | Low background noise (home/office) | 1,017 | 1 | 48,000 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 48 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | French (France) scripted telephony | |
38 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 305 hours | Add Quote | French SpeechDat(II) FDB-5000 | Nuance | Scripted Speech | French | France | Low background noise | 5,040 | 1 | 237,000 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 47 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | French (France) scripted telephony | |
60 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 45 hours | Add Quote | Luxembourgish French SpeechDat(II) FDB-500 (FIXED1LF) | Nuance | Scripted Speech | French | Luxembourg | Low background noise | 614 | 1 | 32,000 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 53 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | French (Luxembourg) telephony | |
189 | Text | ASR, TTS, Language Modelling | N/A | 146,000 words | Add Quote | deu_DEU_PHON | Appen Global | Pronunciation Dictionary | German | Germany | N/A | N/A | N/A | N/A | 146,000 | N/A | text | German (Germany) Pronunciation Dictionary | ||
16 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 16 hours | Add Quote | DEU_ASR001 | Appen Global | Scripted Speech | German | Germany | Low background noise (studio) | 127 | 2 | 12,700 | 6,826 | 16 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words Each speaker read 100 prompts including digits, natural numbers, personal and city names, telephone numbers, generic command and control items, phonetically rich sentences and words | German (Germany) scripted microphone | |
18 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 25 hours | Add Quote | DEU_ASR003 | Global Phone | Scripted Speech | German | Germany | Low background noise (home/office) | 77 | 1 | 10,085 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | German (Germany) scripted microphone | |
42 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 31 hours | Add Quote | German SpeechDat (II) FDB-1000 | Nuance | Scripted Speech | German | Germany | Low background noise (home/office) | 988 | 1 | 43,000 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 44 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | German (Germany) telephony | |
43 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 268 hours | Add Quote | German SpeechDat(II) FDB-4000 | Nuance | Scripted Speech | German | Germany | Low background noise (home/office) | 4,000 | 1 | 160,000 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 40 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | German (Germany) telephony | |
61 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 33 hours | Add Quote | Luxembourgish German SpeechDat(II) FDB-500 (FIXED1LG) | Nuance | Scripted Speech | German | Luxembourg | Low background noise | 500 | 1 | 26,500 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 53 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | German (Luxembourg) telephony | |
190 | Text | ASR, TTS, Language Modelling | N/A | 15,000 words | Add Quote | deu_CHE_PHON | Appen Global | Pronunciation Dictionary | German | Switzerland | N/A | N/A | N/A | N/A | 15,000 | N/A | text | German (Switzerland) Pronunciation Dictionary | ||
94 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 53 hours | Add Quote | Speecon German (Switzerland) database | Nuance | Scripted Speech | German | Switzerland | Mixed (office, entertainment, car, public place) | 600 (550 adult speakers and 50 child speakers) | 4 | 170,000 | Available on request | 16 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 290 prompts per adult speaker and 210 prompts per child speaker including digits, natural numbers, letter strings, personal, place and business names, application words for adult speakers, command (toy, phone and general) for child speakers, phonetically rich words and sentences and free and elicited spontaneous responses for adult speakers | German (Switzerland) scripted microphone | |
68 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone and landline | 31 hours | Add Quote | OrienTel German Spoken by Turkish | Nuance | Scripted Speech | German | Turkey | Low background noise | 300 | 1 | 15,600 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 52 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | German (Turkey) telephony | |
191 | Text | ASR, TTS, Language Modelling | N/A | 5,000 words | Add Quote | ell_GRC_PHON | Appen Global | Pronunciation Dictionary | Greek | Greece | N/A | N/A | N/A | N/A | 5,000 | N/A | text | Greek (Greece) Pronunciation Dictionary | ||
118 | Audio | ASR, Virtual Assistant, Chatbot | Mobile phone | 191 hours | Add Quote | GRE_ASR001_CN | Appen China | Scripted Speech | Greek | Greece | Low background noise (home/office) | 287 | 1 | 54,113 | 68,271 | 16 | wav | Dataset is fully transcribed | Greek (Greece) scripted smartphone | |
192 | Text | ASR, TTS, Language Modelling | N/A | 35,000 words | Add Quote | grn_PRY_PHON | Appen Global | Pronunciation Dictionary | Guarani | Paraguay | N/A | N/A | N/A | N/A | 35,000 | N/A | text | Guarani (Paraguay) Pronunciation Dictionary | ||
194 | Text | ASR, TTS, Language Modelling | N/A | 15,000 words | Add Quote | hat_HTI_PHON | Appen Global | Pronunciation Dictionary | Haitian Creole | Haiti | N/A | N/A | N/A | N/A | 15,000 | N/A | text | Haitian Creole (Haiti) Pronunciation Dictionary | ||
45 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone | 33 hours | Add Quote | HAU_ASR002 | Appen Global | Conversational Speech | Hausa | Nigeria | Low background noise | 200 | 2 | Available on request | 7,949 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words 200 telephony conversations are recorded for this project - 100 speakers make 2 calls each (1 from landline, 1 from mobile) to a pool of 100 call receivers | Hausa (Nigeria) conversational telephony | |
195 | Text | ASR, TTS, Language Modelling | N/A | 11,000 words | Add Quote | hau_NGA_PHON | Appen Global | Pronunciation Dictionary | Hausa | Nigeria | N/A | N/A | N/A | N/A | 11,000 | N/A | text | Hausa (Nigeria) Pronunciation Dictionary | ||
44 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 20 hours | Add Quote | HAU_ASR001 | Global Phone | Scripted Speech | Hausa | Multiple | Low background noise (home/office) | 103 | 1 | 7,895 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | Hausa scripted microphone | |
46 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 34 hours | Add Quote | HEB_ASR001 | Appen Global | Conversational Speech | Hebrew | Israel | Low background noise | 200 | 2 | Available on request | 19,250 | 8 | alaw or wav | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words 200 telephony conversations are recorded for this project - 100 speakers make 2 calls each (1 from landline, 1 from mobile) to a pool of 100 call receivers | Hebrew (Israel) conversational telephony | |
196 | Text | ASR, TTS, Language Modelling | N/A | 31,000 words | Add Quote | heb_ISR_PHON | Appen Global | Pronunciation Dictionary | Hebrew | Israel | N/A | N/A | N/A | N/A | 31,000 | N/A | text | Hebrew (Israel) Pronunciation Dictionary | ||
48 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 32 hours | Add Quote | HIN_ASR002 | Appen Global | Conversational Speech | Hindi | India | Mixed | 996 | 2 | Available on request | 12,266 | 8 | wav | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words For the majority of calls, both speakers (in-line/out-line) were collected and transcribed, however, for a smaller number of calls, only one half of the conversation was collected and transcribed | Hindi (India) conversational telephony | |
197 | Text | ASR, TTS, Language Modelling | N/A | 35,000 words | Add Quote | hin_IND_PHON | Appen Global | Pronunciation Dictionary | Hindi | India | N/A | N/A | N/A | N/A | 35,000 | N/A | text | Hindi (India) Pronunciation Dictionary | ||
47 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone | 224 hours | Add Quote | HIN_ASR001 | Appen Global | Scripted Speech | Hindi | India | Low background noise | 1,920 | 1 | 96,000 | 9,853 | 8 | alaw | Fully transcribed to SpeechDAT type conventions Dataset is accompanied by a pronunciation lexicon [SAMPA] containing all transcribed words 50 prompts per speaker including digits, natural numbers, personal, business and place names, web addresses, confirmation items (yes, no + fuzzy), generic command and control items, phonetically rich sentences and words | Hindi (India) scripted telephony | |
129 | Video | Fitness Applications, Action Classification, Gesture Recognition | Mobile phone | 2000 videos | Add Quote | VED_HUMAN_BODY_CN | Appen China | Human Body | N/A | China | Mixed background and lighting conditions | 1000 | NA | NA | NA | NA | mp4 | Video clips are approximately 10-20 seconds long | Human body movement | |
198 | Text | ASR, TTS, Language Modelling | N/A | 500 words | Add Quote | hun_HUN_PHON | Appen Global | Pronunciation Dictionary | Hungarian | Hungary | N/A | N/A | N/A | N/A | 500 | N/A | text | Hungarian (Hungary) Pronunciation Dictionary | ||
119 | Audio | ASR, Virtual Assistant, Chatbot | Mobile phone | 286 hours | Add Quote | HUN_ASR001_CN | Appen China | Scripted Speech | Hungarian | Hungary | Low background noise (home/office) | 254 | 1 | 94,031 | 201,921 | 16 | wav | Dataset is fully transcribed | Hungarian (Hungary) scripted smartphone | |
49 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 65 hours | Add Quote | Hungarian SpeechDat(E) | Nuance | Scripted Speech | Hungarian | Hungary | Low background noise | 1,000 | 1 | 48,000 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 48 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | Hungarian (Hungary) scripted telephony | |
199 | Text | ASR, TTS, Language Modelling | N/A | 30,000 words | Add Quote | ibo_NGA_PHON | Appen Global | Pronunciation Dictionary | Igbo | Nigeria | N/A | N/A | N/A | N/A | 30,000 | N/A | text | Igbo (Nigeria) Pronunciation Dictionary | ||
152 | Text | ASR, TTS, Language Modelling | N/A | 10,000 words | Add Quote | ind_IDN_POS | Appen Global | Part of Speech Dictionary | Indonesian | Indonesia | N/A | N/A | N/A | N/A | 10,000 | N/A | text | Indonesian (Indonesia) Part of Speech Dictionary | ||
151 | Text | ASR, TTS, Language Modelling | N/A | 95,000 words | Add Quote | ind_IDN_PHON | Appen Global | Pronunciation Dictionary | Indonesian | Indonesia | N/A | N/A | N/A | N/A | 95,000 | N/A | text | Indonesian (Indonesia) Pronunciation Dictionary | ||
183 | Text | ASR, TTS, Language Modelling | N/A | 1,400,000 words | Add Quote | pes_IRN_POS | Appen Global | Part of Speech Dictionary | Iranian Persian | Iran | N/A | N/A | N/A | N/A | 1,400,000 | N/A | text | Iranian Persian (Iran) Part of Speech Dictionary | ||
182 | Text | ASR, TTS, Language Modelling | N/A | 80,000 words | Add Quote | pes_IRN_PHON | Appen Global | Pronunciation Dictionary | Iranian Persian | Iran | N/A | N/A | N/A | N/A | 80,000 | N/A | text | Iranian Persian (Iran) Pronunciation Dictionary | ||
52 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 36 hours | Add Quote | ITA_ASR003 | Appen Global | Conversational Speech | Italian | Italy | Low background noise | 200 | 2 | Available on request | 18,974 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words 200 telephony conversations are recorded for this project - 100 speakers make 2 calls each (1 from landline, 1 from mobile) to a pool of 100 call receivers | Italian (Italy) conversational telephony | |
201 | Text | ASR, TTS, Language Modelling | N/A | 147,000 words | Add Quote | ita_ITA_POS | Appen Global | Part of Speech Dictionary | Italian | Italy | N/A | N/A | N/A | N/A | 147,000 | N/A | text | Italian (Italy) Part of Speech Dictionary | ||
200 | Text | ASR, TTS, Language Modelling | N/A | 197,000 words | Add Quote | ita_ITA_PHON | Appen Global | Pronunciation Dictionary | Italian | Italy | N/A | N/A | N/A | N/A | 197,000 | N/A | text | Italian (Italy) Pronunciation Dictionary | ||
50 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 44 hours | Add Quote | ITA_ASR001 | Appen Global | Scripted Speech | Italian | Italy | Mixed | 200 | 4 | 40,000 | 7,316 | 22 | alaw | Fully transcribed to SpeechDAT type conventions Dataset is accompanied by a pronunciation lexicon containing all transcribed words 200 prompts per speaker including 100 command and control type items and 100 phonetically rich sentences | Italian (Italy) scripted microphone | |
51 | Audio | ASR, Virtual Assistant, In Car HMI & Entertainment | Microphone | 47 hours | Add Quote | ITA_ASR002 | Appen Global | Scripted Speech | Italian | Italy | Mixed (in-car) | 103 | 4 | 35,875 | 10,366 | 48 | alaw | Fully transcribed to SpeechDAT type conventions Dataset is accompanied by a pronunciation lexicon containing all transcribed words 350 prompts per speaker including digits, street names, generic command and control items, phonetically rich sentences and words Each speaker recorded 1or 2 sessions including Session 1 in a parked vehicle with the engine running and Session 2 in a vehicle travelling at 60 mph (100 km/h) | Italian (Italy) scripted microphone | |
53 | Audio | TTS | Microphone | 3 hours | Add Quote | ITA_TTS001 | Appen Global | Scripted Speech | Italian | Italy | Low background noise (studio) | 1 | 1 | 3,300 | Available on request | 22 | alaw | Dataset is accompanied by a pronunciation lexicon containing all words spoken in the Dataset 3,300 prompts per speaker including phonetically rich sentences | Italian (Italy) scripted microphone | |
54 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 38 hours | Add Quote | Italian Fixed Network Speech SpeechDat(M) Corpus | Nuance | Scripted Speech | Italian | Italy | Low background noise (home/office) | 1,000 | 1 | 39,000 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 39 prompts per speaker includign isolated and connected digits, natural numbers, money amounts, spelled words, time and date phrases, yes/no questions, city names, common application words, application words in phrases and phonetically rich sentences | Italian (Italy) telephony | |
55 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 228 hours | Add Quote | Italian SpeechDat(II) FDB-3000 | Nuance | Scripted Speech | Italian | Italy | Low background noise (home/office) | 3,040 | 1 | 134,000 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 44 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | Italian (Italy) telephony | |
56 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone | 103 hours | Add Quote | Italian SpeechDat(II) MDB-250 | Nuance | Scripted Speech | Italian | Italy | Low background noise (home/office) | 375 | 1 | 19,000 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 51 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | Italian (Italy) telephony | |
89 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone | 13 hours | Add Quote | SpeechDat(M) Italian Mobile Network Speech Database | Nuance | Scripted Speech | Italian | Italy | Low background noise (home/office) | 342 | 1 | 13,500 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 40 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | Italian (Italy) telephony | |
203 | Text | ASR, TTS, Language Modelling | N/A | 265,000 words | Add Quote | jpn_JPN_POS | Appen Global | Part of Speech Dictionary | Japanese | Japan | N/A | N/A | N/A | N/A | 265,000 | N/A | text | Japanese (Japan) Part of Speech Dictionary | ||
202 | Text | ASR, TTS, Language Modelling | N/A | 262,000 words | Add Quote | jpn_JPN_PHON | Appen Global | Pronunciation Dictionary | Japanese | Japan | N/A | N/A | N/A | N/A | 262,000 | N/A | text | Japanese (Japan) Pronunciation Dictionary | ||
57 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 33 hours | Add Quote | JPN_ASR001 | Global Phone | Scripted Speech | Japanese | Japan | Low background noise (home/office) | 144 | 1 | 13,067 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | Japanese (Japan) scripted microphone | |
95 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 57 hours | Add Quote | Speecon Japanese | Nuance | Scripted Speech | Japanese | Japan | Mixed (office, entertainment, car, public place) | 600 (550 adult speakers and 50 child speakers) | 4 | 170,000 | Available on request | 16 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 290 prompts per adult speaker and 210 prompts per child speaker including digits, natural numbers, letter strings, personal, place and business names, application words for adult speakers, command (toy, phone and general) for child speakers, phonetically rich words and sentences and free and elicited spontaneous responses for adult speakers | Japanese (Japan) scripted microphone | |
136 | Text | NER, Content Classification, Search Engines | N/A | 20,629 sentences | Add Quote | JPY_NER001 | Appen Global | News NER | Japanese | Japan | N/A | N/A | N/A | 20,629 | Available on request | N/A | text | Japanese NER news text | ||
204 | Text | ASR, TTS, Language Modelling | N/A | 20,000 words | Add Quote | jav_IDN_PHON | Appen Global | Pronunciation Dictionary | Javanese | Indonesia | N/A | N/A | N/A | N/A | 20,000 | N/A | text | Javanese (Indonesia) Pronunciation Dictionary | ||
58 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 15 hours | Add Quote | KAN_ASR001 | Appen Global | Conversational Speech | Kannada | India | Mixed | 178 | 2 | Available on request | 15,660 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words | Kannada (India) conversational telephony | |
109 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 57 hours | Add Quote | KAN_ASR001A | Appen Global | Conversational Speech | Kannada | India | Mixed | 1,000 | 2 | Available on request | 15,660 | 8 | alaw | Approx. 25% of the dataset sessions are transcribed and time stamped - full transcripts can be made available Database is accompanied by a pronunciation lexicon containing all transcribed words | Kannada (India) conversational telephony | |
205 | Text | ASR, TTS, Language Modelling | N/A | 35,000 words | Add Quote | kan_IND_PHON | Appen Global | Pronunciation Dictionary | Kannada | India | N/A | N/A | N/A | N/A | 35,000 | N/A | text | Kannada (India) Pronunciation Dictionary | ||
206 | Text | ASR, TTS, Language Modelling | N/A | 30,000 words | Add Quote | kaz_KAZ_PHON | Appen Global | Pronunciation Dictionary | Kazakh | Kazakhstan | N/A | N/A | N/A | N/A | 30,000 | N/A | text | Kazakh (Kazakhstan) Pronunciation Dictionary | ||
123 | Audio | ASR, Virtual Assistant, Chatbot | Mobile phone | 90 hours | Add Quote | KHM_ASR001_CN | Appen China | Scripted Speech | Central Khmer | Cambodia | Low background noise (home/office) | 94 | 1 | 24,618 | 52,157 | 16 | wav | Dataset is fully transcribed | Khmer (Cambodia) scripted smartphone | |
208 | Text | ASR, TTS, Language Modelling | N/A | 100,000 words | Add Quote | kor_KOR_POS | Appen Global | Part of Speech Dictionary | Korean | South Korea | N/A | N/A | N/A | N/A | 100,000 | N/A | text | Korean (South Korea) Part of Speech Dictionary | ||
207 | Text | ASR, TTS, Language Modelling | N/A | 100,000 words | Add Quote | kor_KOR_PHON | Appen Global | Pronunciation Dictionary | Korean | South Korea | N/A | N/A | N/A | N/A | 100,000 | N/A | text | Korean (South Korea) Pronunciation Dictionary | ||
59 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 20 hours | Add Quote | KOR_ASR001 | Global Phone | Scripted Speech | Korean | South Korea | Low background noise (home/office) | 100 | 1 | 8,107 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | Korean (South Korea) scripted microphone | |
132 | Text | NER, Content Classification, Search Engines | N/A | 25,830 sentences | Add Quote | KOR_NER001 | Appen Global | News NER | Korean | South Korea | N/A | N/A | N/A | 25,830 | Available on request | N/A | text | Korean NER news text | ||
209 | Text | ASR, TTS, Language Modelling | N/A | 60,000 words | Add Quote | kur_TUR_PHON | Appen Global | Pronunciation Dictionary | Kurmanji | Turkey | N/A | N/A | N/A | N/A | 60,000 | N/A | text | Kurmanji (Turkey) Pronunciation Dictionary | ||
210 | Text | ASR, TTS, Language Modelling | N/A | 9,000 words | Add Quote | lao_LAO_PHON | Appen Global | Pronunciation Dictionary | Lao | Laos | N/A | N/A | N/A | N/A | 9,000 | N/A | text | Lao (Laos) Pronunciation Dictionary | ||
211 | Text | ASR, TTS, Language Modelling | N/A | 60,000 words | Add Quote | lit_LTU_PHON | Appen Global | Pronunciation Dictionary | Lithuanian | Lithuania | N/A | N/A | N/A | N/A | 60,000 | N/A | text | Lithuanian (Lithuania) Pronunciation Dictionary | ||
212 | Text | ASR, TTS, Language Modelling | N/A | 4,000 words | Add Quote | mal_IND_PHON | Appen Global | Pronunciation Dictionary | Malayalam | India | N/A | N/A | N/A | N/A | 4,000 | N/A | text | Malayalam (India) Pronunciation Dictionary | ||
213 | Text | ASR, TTS, Language Modelling | N/A | 10,000 words | Add Quote | msa_MYS_PHON | Appen Global | Pronunciation Dictionary | Malaysian | Malaysia | N/A | N/A | N/A | N/A | 10,000 | N/A | text | Malaysian (Malaysia) Pronunciation Dictionary | ||
214 | Text | ASR, TTS, Language Modelling | N/A | 35,000 words | Add Quote | zho_CHN_PHON | Appen Global | Pronunciation Dictionary | Mandarin (Simplified) | China | N/A | N/A | N/A | N/A | 35,000 | N/A | text | Mandarin (Simplified) (China) Pronunciation Dictionary | ||
215 | Text | ASR, TTS, Language Modelling | N/A | 50,000 words | Add Quote | zho_TWN_PHON | Appen Global | Pronunciation Dictionary | Mandarin (Traditional) | Taiwan | N/A | N/A | N/A | N/A | 50,000 | N/A | text | Mandarin (Traditional) (Taiwan) Pronunciation Dictionary | ||
63 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 26 hours | Add Quote | MAC_ASR002 | Global Phone | Scripted Speech | Mandarin Chinese | China | Low background noise (home/office) | 132 | 1 | 10,225 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | Mandarin Chinese (China) scripted microphone | |
62 | Audio | ASR, Call Centre, Virtual Assistant | Mobile phone and landline | 323 hours | Add Quote | MAC_ASR001 | Appen Global | Scripted Speech | Mandarin Chinese | China | Mixed | 2,000 | 1 | 200,000 | 7,145 | 8 | alaw | Fully transcribed to SpeechDAT type conventions Dataset is accompanied by a pronunciation lexicon [SAMPA] containing all transcribed words 98 prompts per speaker including digits, natural numbers, letter strings, personal, place, and business names, confirmation items (yes, no + fuzzy), generic command and control items (from a set of 215), phonetically rich sentences and words | Mandarin Chinese (China) scripted telephony | |
134 | Text | NER, Content Classification, Search Engines | N/A | 17,313 sentences | Add Quote | MAC_NER001 | Appen Global | News NER | Mandarin Chinese | China | N/A | N/A | N/A | 17,313 | Available on request | N/A | text | Mandarin NER news text | ||
64 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 15 hours | Add Quote | MAR_ASR001 | Appen Global | Conversational Speech | Marathi | India | Mixed | 180 | 2 | Available on request | 11,908 | 8 | alaw | Approx. 29% of the dataset sessions are transcribed and time stamped - full transcripts can be made available Dataset is accompanied by a pronunciation lexicon containing all transcribed words | Marathi (India) conversational telephony | |
110 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 52 hours | Add Quote | MAR_ASR001A | Appen Global | Conversational Speech | Marathi | India | Mixed | 1,000 | 2 | Available on request | 11,908 | 8 | alaw | Portion of the dataset sessions are transcribed and time stamped - full transcripts can be made available Dataset is accompanied by a pronunciation lexicon containing all transcribed words | Marathi (India) conversational telephony | |
216 | Text | ASR, TTS, Language Modelling | N/A | 30,000 words | Add Quote | mar_IND_PHON | Appen Global | Pronunciation Dictionary | Marathi | India | N/A | N/A | N/A | N/A | 30,000 | N/A | text | Marathi (India) Pronunciation Dictionary | ||
217 | Text | ASR, TTS, Language Modelling | N/A | 30,000 words | Add Quote | mon_MNG_PHON | Appen Global | Pronunciation Dictionary | Mongolian | Mongolia | N/A | N/A | N/A | N/A | 30,000 | N/A | text | Mongolian (Mongolia) Pronunciation Dictionary | ||
219 | Text | ASR, TTS, Language Modelling | N/A | 3,000 words | Add Quote | nor_NOR_POS | Appen Global | Part of Speech Dictionary | Norwegian | Norway | N/A | N/A | N/A | N/A | 3,000 | N/A | text | Norwegian (Norway) Part of Speech Dictionary | ||
218 | Text | ASR, TTS, Language Modelling | N/A | 115,000 words | Add Quote | nor_NOR_PHON | Appen Global | Pronunciation Dictionary | Norwegian | Norway | N/A | N/A | N/A | N/A | 115,000 | N/A | text | Norwegian (Norway) Pronunciation Dictionary | ||
220 | Text | ASR, TTS, Language Modelling | N/A | 15,000 words | Add Quote | ori_IND_PHON | Appen Global | Pronunciation Dictionary | Oriya | India | N/A | N/A | N/A | N/A | 15,000 | N/A | text | Oriya (India) Pronunciation Dictionary | ||
80 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 20 hours | Add Quote | PAP_ASR001 | Appen Global | Conversational Speech | Panjabi | Pakistan | Low background noise | 205 | 2 | Available on request | 7,298 | 8 | alaw | Dataset is fully transcribed and time-stamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words 71% of calls, both speakers (in-line/out-line) were collected and transcribed, however, for 29% calls, only one half of the conversation was collected and transcribed | Panjabi (Pakistan) conversational telephony | |
74 | Audio | ASR, Automatic Captioning, Keyword Spotting | Microphone | 51 hours | Add Quote | PAS_BRC001 | Appen Global | Broadcast Speech | Northern Pashto; Southern Pashto | Afghanistan | Low background noise (studio) | N/A | 1 | Available on request | Available on request | N/A | wav | Dataset is fully transcribed and timestamped Dataset is largely speech only and does not include music or advertisements Data types include: talk shows, interviews, news broadcasts (excluding news reading by anchors) | Pashto (Afghanistan) broadcast | |
73 | Audio | ASR, Conversational AI, Speech Analytics | Microphone | 39 hours | Add Quote | PAS_ASR002 | Appen Global | Conversational Speech | Northern Pashto; Southern Pashto | Afghanistan | Low background noise | 40 | 2 | Available on request | 9,480 | 16 | wav | Dataset is fully transcribed and time stamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words A full translation of the transcripts into French is also available as an optional additional purchase Average length of calls: 120 mins where one speaker acts as an interviewer and the other as the interviewee for scenarios are similar to TransTAC style (e.g. civil affairs, checkpoints etc.) The interviewer appears in more than one set of dialogues but the interviewee is unique for each set | Pashto (Afghanistan) conversational microphone | |
72 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 55 hours | Add Quote | PAS_ASR001 | Appen Global | Conversational Speech | Northern Pashto; Southern Pashto | Afghanistan | Low background noise | 967 | 2 | Available on request | 13,633 | 8 | wav | Dataset is fully transcribed and time stamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words For the majority of calls, both speakers (in-line/out-line) were collected and transcribed, however, for a smaller number of calls, only one half of the conversation was collected and transcribed | Pashto (Afghanistan) conversational telephony | |
221 | Text | ASR, TTS, Language Modelling | N/A | 65,000 words | Add Quote | pus_AFG_PHON | Appen Global | Pronunciation Dictionary | Pashto | Afghanistan | N/A | N/A | N/A | N/A | 65,000 | N/A | text | Pashto (Afghanistan) Pronunciation Dictionary | ||
223 | Text | ASR, TTS, Language Modelling | N/A | 4,000 words | Add Quote | pol_POL_POS | Appen Global | Part of Speech Dictionary | Polish | Poland | N/A | N/A | N/A | N/A | 4,000 | N/A | text | Polish (Poland) Part of Speech Dictionary | ||
222 | Text | ASR, TTS, Language Modelling | N/A | 40,000 words | Add Quote | pol_POL_PHON | Appen Global | Pronunciation Dictionary | Polish | Poland | N/A | N/A | N/A | N/A | 40,000 | N/A | text | Polish (Poland) Pronunciation Dictionary | ||
75 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 25 hours | Add Quote | POL_ASR001 | Global Phone | Scripted Speech | Polish | Poland | Low background noise (home/office) | 99 | 1 | 10,130 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | Polish (Poland) scripted microphone | |
120 | Audio | ASR, Virtual Assistant, Chatbot | Mobile phone | 293 hours | Add Quote | POL_ASR002_CN | Appen China | Scripted Speech | Polish | Poland | Low background noise (home/office) | 353 | 1 | 106,674 | 168,544 | 16 | wav | Dataset is fully transcribed | Polish (Poland) scripted smartphone | |
76 | Audio | ASR, Call Centre, Virtual Assistant | Landline only | 78 hours | Add Quote | Polish SpeechDat(E) Database | Nuance | Scripted Speech | Polish | Poland | Low background noise | 1,000 | 1 | 48,000 | Available on request | 8 | Available on request | Dataset is fully transcribed to SpeechDAT type conventions and is accompanied by a pronunciation lexicon and validation report 48 prompts per speaker including digits, natural numbers, letter strings, personal, place and business names, confirmation items (yes, no + fuzzy), generic command and control items and phonetically rich sentences and words | Polish (Poland) scripted telephony | |
78 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 33 hours | Add Quote | PTB_ASR002 | Appen Global | Conversational Speech | Portuguese | Brazil | Low background noise | 200 | 2 | Available on request | 11,287 | 8 | alaw | Dataset is fully transcribed and time stamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words | Portuguese (Brazil) conversational telephony | |
77 | Audio | ASR, Virtual Assistant, Chatbot | Microphone | 26 hours | Add Quote | PTB_ASR001 | Global Phone | Scripted Speech | Portuguese | Brazil | Low background noise (home/office) | 102 | 1 | 10,417 | Available on request | 16 | wav | Dataset is fully transcribed and the transcription is available both in original script and in Romanized form Each speaker reads a number of phonetically rich sentences selected from national newspaper articles available from the web tocover a wide domain with large vocabulary Developed in collaboration with the Karlsruhe Institute of Technology (KIT) | Portuguese (Brazil) microphone | |
225 | Text | ASR, TTS, Language Modelling | N/A | 100,000 words | Add Quote | por_BRA_POS | Appen Global | Part of Speech Dictionary | Portuguese | Brazil | N/A | N/A | N/A | N/A | 100,000 | N/A | text | Portuguese (Brazil) Part of Speech Dictionary | ||
224 | Text | ASR, TTS, Language Modelling | N/A | 102,000 words | Add Quote | por_BRA_PHON | Appen Global | Pronunciation Dictionary | Portuguese | Brazil | N/A | N/A | N/A | N/A | 102,000 | N/A | text | Portuguese (Brazil) Pronunciation Dictionary | ||
79 | Audio | ASR, Conversational AI, Speech Analytics | Mobile phone and landline | 36 hours | Add Quote | PTP_ASR001 | Appen Global | Conversational Speech | Portuguese | Portugal | Low background noise | 200 | 2 | Available on request | 16,339 | 8 | alaw | Dataset is fully transcribed and timestamped Dataset is accompanied by a pronunciation lexicon containing all transcribed words 200 telephony conversations are recorded for this project - 100 speakers make 2 calls each (1 from landline, 1 from mobile) to a pool of 100 call receivers | Portuguese (Portugal) conversational telephony | |
227 | Text | ASR, TTS, Language Modelling | N/A | 100,000 words | Add Quote | por_PRT_POS | Appen Global | Part of Speech Dictionary | Portuguese | Portugal | N/A | N/A | N/A | N/A | 100,000 | N/A | text | Portuguese (Portugal) Part of Speech Dictionary | ||
226 | Text | ASR, TTS, Language Modelling | N/A | 112,000 words | Add Quote | por_PRT_PHON | Appen Global | Pronunciation Dictionary | Portuguese | Portugal | N/A | N/A | N/A | N/A | 112,000 | N/A | text | Portuguese (Portugal) Pronunciation Dictionary | ||
81 |