Hi! I'm from the RWTH team (
https://paperswithcode.com/sota/speech-recognition-on-libris...). Our best system (2.3% WER) (trained only on the Librispeech data, i.e. much less data than your model) currently is a hybrid HMM/NN model, and you are right, the acoustic model uses a BLSTM. However, we have shown in other work (
https://www-i6.informatik.rwth-aachen.de/publications/downlo...) that you can expect similar performance in an online system, or not much worse (there is also many related work, which often is maybe 5-10% relative worse than the offline system).
I guess you prefer an "end-to-end" model over a hybrid HMM/NN model, for simplicity, right? As far as I remember, you use CTC, right? I always wondered why you have chosen CTC, and not some better model, like RNN-T, RNA, or some of the streaming attention variants. They should give you all the same properties (online capable, low latency, simple, end-to-end), but much better WER performance. Or is this simply because there currently is no simple ready-to-use implementation for those? Note that we published some TF code recently for some streaming attention variants, and plan to publish some RNN-T/RNA code soon.
I guess you prefer an "end-to-end" model over a hybrid HMM/NN model, for simplicity, right? As far as I remember, you use CTC, right? I always wondered why you have chosen CTC, and not some better model, like RNN-T, RNA, or some of the streaming attention variants. They should give you all the same properties (online capable, low latency, simple, end-to-end), but much better WER performance. Or is this simply because there currently is no simple ready-to-use implementation for those? Note that we published some TF code recently for some streaming attention variants, and plan to publish some RNN-T/RNA code soon.