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Comment by dylanbfox

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Comment by dylanbfox

Great question. This is technically referred to as "Wake Word Detection". You run a really small model locally that is just processing 500ms (for example) of audio at a time through a light weight CNN or RNN. The idea here is that it's just binary classification (vs actual speech recognition).

There are some open source libraries that make this relatively easy:

- https://github.com/Kitt-AI/snowboy (looks to be shutdown now) - https://github.com/cmusphinx/pocketsphinx

This avoids having to stream audio 24x7 to a cloud model which would be super expensive. This being said, I'm pretty sure what the Alexa does, for example, is send any positive wake word to a cloud model (that is bigger and more accurate) to verify the prediction of the local wake word detection model AFAIK.

Once you are positive you have a positive wake word detected - that's when you start streaming to an accurate cloud based transcription model like Assembly to minimize costs!

Replies dylanbfox · 2021-10-07
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