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Comparing the Accuracy of Automatic Transcription Services

How do the different automatic transcription services compare in accuracy? And are any of them good enough for your audio? In the last few years, automatic speech recognition has gotten really good. Computer models now understand speech as well as humans (with the right type of audio). From Mary Meeker’s 2017 Internet Trends Report It costs $1+ per minute get something hand-transcribed, so automation makes transcription affordable for all types of applications that couldn’t otherwise justify the cost.… Read more

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Evaluate speech-to-text services from AWS, Microsoft and Google

Amazon Transcribe, Microsoft Azure Speech to Text and Google Cloud Speech-to-Text enable developers to create dictation applications that can automatically generate transcriptions for audio files, as well as captions for video files. Development teams can weave these capabilities into timesaving apps for a range of uses, including call center analytics, video indexing services, web conference indexing and business transcription workflows.… Read more

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State of the Market: Voice dictation APIs

Transcribe-bot monster meltdown: DeepSpeech, Dragon, Google, IBM, MS, and more! Speech has been a near-impossible field for computers until recently, and as talking to my computer has been something I dreamed of as a kid, I have been tracking the field as it progressed trough the years. Expectations from dictation are high, correcting more than two words per paragraph means to most people it is just easier to type the whole thing in.Microphone… Read more

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Comparison of Google Cloud Speech Model vs Google Cloud Video Model

I had an audio clip (length – 32 min, 21 seconds) that I ran through both the Google Cloud’s Speech-to-Text Model and Google Cloud’s Video-to-Text Model. Here are the results. Total # of words: 3,956Total # of characters: 20,970Total # of pages: 7 Speech Model Total # of errors: 102 Accuracy: 97.42 % Video Model Total # of errors: 9Accuracy: 99.77 %… Read more