Kaldi has 4.14% WER (95.86% accuracy) on the same test dataset (test-clean) [1] using a model that runs faster than real time on CPU. You would have to make the

Kaldi has 4.14% WER (95.86% accuracy) on the same test dataset (test-clean) [1] using a model that runs faster than real time on CPU. You would have to make the model smaller to run it in real time on a RasPi3, but according to this [2], you can get decent WERs for read speech even then. [1] https://github.com/kaldi-asr/kaldi/blob/master/egs/librispeech/s5/local/chain/tuning/run_tdnn_1b.sh https://github.com/kaldi-asr/kaldi/blob/master/egs/librispee... [2] https://groups.google.com/d/msg/kaldi-help/Pr6jPH1Qshg/kn8df0JmBQAJ https://groups.google.com/d/msg/kaldi-help/Pr6jPH1Qshg/kn8df...