State-of-the-art Speech Recognition With Sequence-to-Sequence Models

TL;DR

This paper advances sequence-to-sequence speech recognition models by integrating structural and optimization improvements, achieving significant WER reductions on voice search and dictation tasks, demonstrating practical and competitive performance.

Contribution

The paper introduces multi-head attention, word piece models, and training techniques to enhance LAS models for challenging speech recognition tasks.

Findings

WER reduced from 9.2% to 5.6% on voice search

Achieved 4.1% WER on dictation

Outperforms conventional systems in accuracy

Abstract

Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In previous work, we have shown that such architectures are comparable to state-of-theart ASR systems on dictation tasks, but it was not clear if such architectures would be practical for more challenging tasks such as voice search. In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance. On the structural side, we show that word piece models can be used instead of graphemes. We also introduce a multi-head attention architecture, which offers improvements over the commonly-used single-head attention. On the optimization side, we explore synchronous training, scheduled sampling, label smoothing, and minimum word error rate optimization, which are all shown to improve accuracy. We present results with a unidirectional LSTM encoder for streaming recognition. On a 12, 500 hour voice search task, we find that the proposed changes improve the WER from 9.2% to 5.6%, while the best conventional system achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to 5% for the conventional system.