arXiv:1610.03022cs.CL
Very Deep Convolutional Networks for End-to-End Speech Recognition
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
Sequence-to-sequence models have shown success in end-to-end speech recognition. However these models have only used shallow acoustic encoder networks. In our work, we successively train very deep convolutional networks to add more expressive power and better generalization for end-to-end ASR models. We apply network-in-network principles, batch normalization, residual connections and convolutional LSTMs to build very deep recurrent and convolutional structures. Our models exploit the spectral structure in the feature space and add computational depth without overfitting issues. We experiment with the WSJ ASR task and achieve 10.5\% word error rate without any dictionary or language using a 15 layer deep network.
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…
sdenton4View on HNParent context