How AI is Making Transcription More EfficientHuman transcription has been around in some form for hundreds, if not thousands, of years. Recently, it’s getting a boost with AI. Transcriptions themselves are the text form of the audio content; they enable a reader to comprehend what was said or what happened during a period of time without having to listen to the audio recording again. Transcriptions are essential for recordkeeping, knowledge sharing, and providing greater accessibility. With advances in AI in the last few years, people are increasingly relying on a technology called automatic speech recognition (ASR) to help with transcriptions. ASR technologies can translate human speech to text with expediency and the market for them is already growing quickly.
Manual vs. AI-powered TranscriptionWe’re all familiar with the manual method of audio transcription: with an in-person situation, a human takes notes as quickly as possible on the words or happenings in a given meeting or event. Remotely, a human may listen to an audio file from the event and transcribe it as they listen. They may then review their initial notes and clean them up as needed. This method can achieve high levels of accuracy, especially in the latter scenario, but is often time-consuming and difficult for the note taker. AI-powered transcription is meant to lower the time investment for this task by handling the initial transcription in real-time. When it works best is when a human validates the document afterward, fixing any errors or misunderstandings by the AI. Ideally, this human should have expertise in the subject matter (law, medicine, etc.) so they can understand the appropriate terminologies to be used. The reason for needing a human expert is because, while AI-powered audio transcription has improved tremendously in recent years, it still faces many challenges when it comes to accuracy.
Real-life Applications of Audio TranscriptionAccurate transcriptions are critical for many industries, while other industries are just starting to adopt transcription practices. Many startups have recently joined the space and are offering AI-powered transcription technology that motivates faster adoption. In any case, here are a few applications where transcription is used:
- Medicine: Doctors and nurses have to keep a large number of detailed records of interactions with patients, treatment plans, prescriptions, and more. With dictation services, they can verbally detail this information and have it automatically transcribed for greater efficiency. The field of medicine relies on precise transcription to ensure they’re treating patients correctly. For example, if the transcription incorrectly notes the number of times a patient needs to take a prescription, it could have disastrous effects on their health.
- Social Media: If you’ve looked at Instagram or YouTube lately, you may have noticed some videos have captioning services. This is a new feature that autocaptions people as they speak using AI. While it may not always be fully accurate, it’s helping to provide greater accessibility and usability for users.
- Technology: Smartphones have had the talk-to-text feature in place for some time. As the name suggests, it lets you text someone through audio dictation rather than manually typing out a message.
- Law: In law, accurate documentation of court proceedings is fundamental to a case because accuracy can affect the outcome of that case. It’s also important for historical documentation to learn from or reference for future cases.
- Police Work: Audio transcription has numerous applications in police work, with likely more to come. It can be used for transcribing investigative interviews, evidence records, calls to the emergency line, body camera recorded interactions, and more. Much like with the law, the accuracy of these transcriptions can have a significant impact on court cases and people’s lives.
Overcoming Challenges in Transcription for Greater InclusivityAI still faces many hurdles in achieving precise transcripts. Much of these have to do with the fact that human speech varies considerably depending on the speaker. For AI to capture a speaker’s dialogue correctly, it needs to be familiar with the speaker’s language, dialect, accent, tone, pitch, and volume. That’s a lot of factors, so you can imagine the amount of training data required to teach these models. It’s essential that companies building audio transcription services take an inclusive approach when it comes to building a training dataset. That means taking all of the potential end users of the product into account and ensuring their variations in speech are reflected in the training data. Without full representation, the technology will struggle to recognize words from certain speakers, creating a frustrating experience for the speaker. In the meantime, the best option for companies is to still incorporate human reviewers into the process.
Expert Insight from Stacey Hawke – Linguistic Project ManagerThink about the purpose of your transcript – what it will be used for, who will access it? There are different styles of transcription to suit different purposes. For example:
- Full verbatim – this transcription style includes every full word that is spoken by each participant, including ums, ers, hesitations, repeated words and false starts. This transcription style is useful where a transcript may be used for evidential purposes, such as in court proceedings or disciplinary proceedings.
- Intelligent verbatim – this transcription style excludes all um, ers, superfluous fillers, repeated words (unless used for emphasis), stutters and stammers. All non-standard language is changed to standard, for example ‘cause to because, ain’t to is not. This transcription style can be helpful for interviews conducted for research purposes, where every single word spoken isn’t required, but a record of what was said is needed.
- Summary – this type of transcription is different to the two listed above. In this style, the audio/video file is listened to by a transcriber and a summary of the speech heard is given. A summary should be an accurate and balanced account of the audio file and contain all of the salient points. Summaries include only formal English, such as do not instead of don’t, was not rather than wasn’t. This transcription style is useful where a shorter more manageable document is required.
- Ensure any equipment that could interfere with the recording is switched off, such as air conditioners.
- Ensure windows and doors are closed so that the recording doesn’t pick up any external noises.
- Ask all speakers to introduce themselves at the beginning of the recording to aide our transcribers with voice identification.
- Encourage one person speaking at a time to avoid talking over participants.
- Emphasize important information such as dates and names so that they can be accurately captured.
What We Can Do For YouAt Appen, we provide secure, confidential transcription services to clients from both the public and private sectors. We offer a variety of services to fit our clients’ needs, including:
- Audio transcription: We use machine learning-powered tools to create a transcript of your meeting that’s then reviewed by highly-skilled transcriptionists.
- Note taking and meeting minutes: Our professional note takers attend your meeting and produce an impartial, accurate summary of what was discussed.
- Audio recording: Our recording technicians capture high-quality audio using professional recording equipment on-site.