Not every solution can do this or makes it easy. For a typical live event, you'll likely want to be able to display real-time transcriptions on in-room monitors or bring in audio from a venue's PA system. If you're ready to add automatic live transcription to your events, what should you look for in a solution? Always keep in mind what you wish to achieve. For instance, it will no longer be necessary to transcribe only part of a conference - say, the keynote speech - rather than the entire program for budgetary reasons. Solutions like these already exist, offering a remedy for long-standing live transcription challenges. What do these results mean for businesses and other organizations in need of live transcription services? Affordable and widely available transcription is now possible for conference presentations, university lectures, church sermons and other events that unfold in real time. If we ran the same tests under the same conditions today, the APIs would only perform better since, as pieces of machine learning, they improve over time. It's worth emphasizing that our testing took place in January 2020. AI also offers distinct advantages today over traditional live transcription services when it comes to cost, availability and consistency. However, the overall performance gap is narrowing and will continue to do so with time.
Human transcriptionists still outdo machines in some respects - for example, when it comes to inferring the meaning of garbled speech.
I suspect this idea comes from past experiences with traditional transcription services, which resulted in pristine transcripts composed without the pressure of time. These results challenge an impression of AI-powered transcription that many organizations might hold - namely, that machines can't come close to humans in terms of performance. Most compelling are our results, which suggest these speech recognition engines can perform similarly to humans in real-time settings while charging far less per hour of transcription (under $10 USD for the APIs versus $60 to $200 USD for humans). I'll spare you the methodological details (our paper discusses these in-depth). Our research compared the performance of three leading speech recognition APIs - Amazon Transcribe, Google Cloud Speech-to-Text and IBM Watson Speech to Text - to a generalized measure of human transcription performance. The results provide a clear picture of the state of these cutting-edge APIs and a compelling answer to the question of whether automatic transcription technology is ready for real time. This is a topic several colleagues and I researched in early 2020 for NAB Show's 74th annual Broadcast Engineering and Information Technology (BEIT) conference. However, the practicality of this comes down to their performance compared to humans.
These APIs present an avenue for addressing the cost and supply barriers that a good number of companies face in securing live transcription services. Google, Amazon, IBM and other big names have released speech recognition APIs that developers can use to build automatic real-time transcription solutions. The difference is that the latter outputs its conclusions of the likely meaning of human speech as text. The technology that enables such services is the same that powers automatic transcription solutions. Of course, there are also smart devices, like Alexa and Google Home, that are able to comprehend a wide range of commands. For example, search engines now let you verbalize queries rather than type them, the former being often preferable on mobile devices with cramped touchscreens. Their development has benefited considerably from the popularity of speech-enabled services.
Automatic transcription solutions that function in real time do exist.
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