mycroft-precise VS kaldi-active-grammar

> It reliably responds to the wakeword ("hey Mycroft") from men, and only responds about 50% of the time to women.

They have instructions on how to train your own version of the wakeword listener.

https://github.com/MycroftAI/mycroft-precise#train-your-own-...

The most important part of Astra is detecting when someone is speaking to her. This is done using a RNN (recurrent neural network) which is implemented by Mycroft's Precise. In order to use this, we need to collect voice data (from many people) of them saying Astra, potentially multiple times, and train a model using it. That's where you come in.

I've never tried it, but theoretically a wake word system like Mycroft Precise or Raven might not care too much whether your "wake word" is a jingle?

https://github.com/dictation-toolbox/dragonfly

https://github.com/daanzu/kaldi-active-grammar

With an open system/engine, you can train your own personal speech model. For kaldi-active-grammar (https://github.com/daanzu/kaldi-active-grammar), you can do so without all that much difficulty, although the process/documentation could certainly use improvement.

I bootstrapped my personal speech model by retaining the commands from me using WSR. My voice is quite abnormal, and it took only 10 hours of speech data to train a model orders of magnitude more accurate than any generic model I've ever used. And of course, I retain much of my usage now with Kaldi, so my model improves more and more over time. A virtuous flywheel!

I have been coding entirely by voice for approximately 10 years now (by hand long before that). Most of that time I have been using the Dragonfly (https://github.com/dictation-toolbox/dragonfly) library to construct my own customized voice coding system. The library is highly flexible and open source, allowing you to easily customize everything to suit what you need to be productive. It is perhaps the power user analogue to Dragon Naturally Speaking. With it, you can certainly be highly productive coding by voice. In fact, I develop kaldi-active-grammar (https://github.com/daanzu/kaldi-active-grammar), a free and open source speech recognition backend usable by Dragonfly, itself entirely by voice. There's also a community of voice coders using Dragonfly and other tools that build on top of it, such as Caster (https://github.com/dictation-toolbox/Caster).

- Demo: https://www.youtube.com/watch?v=Qk1mGbIJx3s / Software: https://github.com/daanzu/kaldi-active-grammar

Far field audio is usually harder for any speech system to get correct, so having a good quality mic and using it nearby will _usually_ help with the transcription quality. As a long time Linux user, I would love to see it get some more powerful voice tools - really hope that this opens up over the next few years. Feel free to drop me an email (on my profile) happy to help with setup on any of the above.

kaldi repo: https://github.com/daanzu/kaldi-active-grammar