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Although some models are end-to-end neural nets, most of the ones in production (and all of the ones that get state of the art results) only use a neural net fo
by adrianbg 9y ago
Although some models are end-to-end neural nets, most of the ones in production (and all of the ones that get state of the art results) only use a neural net for one part of the process. Lots of people are as surprised as you, but that's the way it is.
Edit: I should say that in state of the art results there tend to be multiple components, including multiple neural nets and the tricky "decode graph" that gok and I are talking about. These are trained separately then get stuck together, as opposed to being trained in an end-to-end fashion.
- woodson 9y agoSeparating acoustic model and decoding graph search makes sense since you would need a huge amount of (correctly!) transcribed speech for training. See, for example, this paper by Google [1], where they used 125,000 hours (after filtering out the badly transcribed ones from the original 500,000 hours of transcribed speech) for training an end-to-end acoustic-to-word model. Good "old-school" DNN acoustic models can already be trained with orders of magnitude less training data (hundreds to thousands of hours). [1] https://arxiv.org/abs/1610.09975 https://arxiv.org/abs/1610.09975
- adrianbg 9y agoYes, exactly. I do wonder whether a similarly good end-to-end system could be trained by constraining the alignments as I've seen done in some papers.