Comment by albertzeyer | Hacker News Reader

Comment by albertzeyer | Hacker News Reader

Are you searching for a complete solution including NLP and an engine to perform actions? Some of these are already posted, like Home Assistant, and Mycroft.

Sphinx is just for the automatic speech recognition (ASR) part. But there are better solutions for that:

Kaldi (https://kaldi-asr.org/) is probably the most comprehensive ASR solution, which yields very competitive state-of-the-art results.

RASR (https://www-i6.informatik.rwth-aachen.de/rwth-asr/) is for non-commercial use only but otherwise similar as Kaldi.

If you want to use a simpler ASR system, nowadays end-to-end models perform quite well. There are quite a huge number of projects which support these:

RETURNN (https://github.com/rwth-i6/returnn) is non-commercial TF-based. (Disclaimer: I'm one of the main authors.)

Lingvo (https://github.com/tensorflow/lingvo), from Google, TF-based.

ESPnet (https://github.com/espnet/espnet), PyTorch/Chainer.

...

Already got the action engine: all the lights, hvac, tv, calculator, computers, etc are all controllable. None require internet now. Or any kind of location services for that matter.

I really just want the speech-to-text. Ideally I'd also like it to recognise who's talking. But that's a bonus.

I'll second the recommendation for Kaldi. It's more complicated to get running vs pocketsphinx, but in my experience Kaldi has better accuracy/lower latency in general cases vs pocketsphinx (assuming caveats below).

https://github.com/gooofy/zamia-speech/ has been training good [acoustic] models which are worth looking at (including training with robustness against noise). They've also got lots of code and docker images and documentation.

pocketsphinx isn't actually that bad to use with their latest acoustic models and small vocabularies (so its utility depends on your exact use case). But it's not generally good with far field mics/dsp processed audio, not really good with noise, and in my experiments quite not as fast as Kaldi.

Better/larger language models in my experience make a world of difference (esp in the general vocab case) for improving accuracy for either of kaldi or pocketsphinx. Nobody really seems to talk about this(?), since everyone always uses the news corpus from like the 80s as the default language model.

I haven't really ever gotten the various ~deepspeech systems working, so I can't speak to them.

I'm happy to feed it plenty of voice logs as well as a training corpus as necessary. Sounds like an interesting journey.

Would love to know more about this and how you've done it

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