Very Deep Convolutional Networks for End-to-End Speech Recognition — mentioned by sdenton4

Sun, 25 Mar 2018 14:58:28 GMT

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.)…

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Very Deep Convolutional Networks for End-to-End Speech Recognition · HackerPapers discussions

Very Deep Convolutional Networks for End-to-End Speech Recognition — mentioned by sdenton4