Seems all the methods in the writeup are APIs (not sure about wit or sphinx), so what's missing is missing locally-run processes like DeepSpeech. But on that same note, I'd like to see greater accuracy comparisons on all these methods, and pricing (googly gets to around $1.44 / recorded hour?) since that's a significant factor.

From prior use, Google's speech API (at least the "video" model) is freakishly accurate compared to DeepSpeech to where I wondered if they used closed captioning to help train their model. But I haven't seen rest of these at work: https://i.imgur.com/cdOlARO.png

afaik, pure DNN models still lag seriously behind 'traditional' HMM-based frameworks augmented by neural networks (using DNNs for specific parts of the pipeline). Last I checked a couple months ago, state of the art for HNN+DNN was something like 6% word error rate (WER). The best Seq2Seq DNN I know of hit 18% WER, dropping to 10% when a secondary language model was integrated in. (my guess is that part of the problem is leaning too heavily on the attention mechanism... a more 'streaming friendly' framework should help reduce the load on the attention mechanism.)

https://arxiv.org/pdf/1610.03022.pdf

This has changed recently, full seq2seq is now matching hybrid models [0].

[0] https://arxiv.org/abs/1712.01769

Oh, thanks! Now I know what I'm reading on the commute tomorrow.

The majority of the APIs mentioned are probably using DNNs. But those are all online-only, so higher-quality offline engines would definitely be an improvement. I wonder how much effort it would require to integrate them into the SpeechRecognition package.

+1