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If you need streaming, then yes, RNNT is a good option. If not, encoder-decoder-attention performs a bit better than RNN-T. Note that there are also approaches
by albertzeyer 7y ago
If you need streaming, then yes, RNNT is a good option. If not, encoder-decoder-attention performs a bit better than RNN-T.
Note that there are also approaches for encoder-decoder-attention to make that streaming capable, e.g. MoChA or hard attention, etc.
Google uses RNN-T on-device. But they are researching on extending it with another encoder-decoder-attention model on-top, to get better performance.
This is a quite active research area, and it has not really settled. But CTC is not really too much relevant anymore, as RNNT is just a better variant.
- woodson 7y agoRecent work on transformer transducers with limited right (and left) context seem to give decent results as well: https://arxiv.org/abs/2002.02562 https://arxiv.org/abs/2002.02562
- p1esk 7y agoI opened the pdf, did ctrl+F for 'github', got zero results. Have you reproduced their "decent results"?
- woodson 7y agoESPnet apparently already has implementations of versions of transformer transducers with RNN-T loss (though, with different network architecture). At least the paper cites results on a freely available dataset, right?
- p1esk 7y agoWhy would you not expect Google to post their code? E.g. the transformer paper had it: https://arxiv.org/abs/1706.03762 https://arxiv.org/abs/1706.03762