RESEARCH

SPEECH & AUDIO

Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions

September 13, 2019

Abstract

We propose a fully convolutional sequence-to-sequence encoder architecture with a simple and efficient decoder. Our model improves WER on LibriSpeech while being an order of magnitude more efficient than a strong RNN baseline. Key to our approach is a time-depth separable convolution block which dramatically reduces the number of parameters in the model while keeping the receptive field large. We also give a stable and efficient beam search inference procedure which allows us to effectively integrate a language model. Coupled with a convolutional language model, our time-depth separable convolution architecture improves by more than 22% relative WER over the best previously reported sequence-to-sequence results on the noisy LibriSpeech test set.

Download the Paper

AUTHORS

Written by

Awni Hannun

Ann Lee

Qiantong Xu

Ronan Collobert

Publisher

Interspeech

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