kaldi-active-grammar

Really thankful for https://github.com/dictation-toolbox/dragonfly along with https://github.com/daanzu/kaldi-active-grammar for voice to text coding

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!

"Everything other than talon has terrible latency": False! I develop kaldi-active-grammar (https://github.com/daanzu/kaldi-active-grammar), a free and open source speech recognition backend, which has extremely low latency. You can adjust how aggressive the VAD (voice activity detection) is to suit your preference, but the speech engine latency can be almost negligible, especially for voice commands (vs prose dictation). However, I agree that "most existing speech recognition engines were not designed with the kind of latency you want for quick one syllable commands", and that low latency is pivotal to being productive with voice commands. I also agree with your other points.

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. However, it does require work to setup and customize to suit you, so it isn't really for the "general population" of computer users to just sit down and use. With regard to accuracy of speech recognition, being open allows you to (with sufficient motivation) to train a custom acoustic speech model that recognizes your voice spec

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-

I am sorry to hear this. I think there are many people in a similar boat to you and there are quite a few people working on command & dictation computing. Although my tool _may_ help you find out which speech systems work well for your voice/accent/mic/vocab it might also be worth trying another one of the specialist libraries specifically for dictation and controlling computers. I've not heard of Almond, but I have seen the following projects which might be helpful: - Dragonfly: https://github.com/dictation-toolbox/dragonfly - 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