State-of-the-Art Speech Recognition Using Multi-Stream Self-Attention With Dilated 1D Convolutions

TL;DR

This paper introduces a multi-stream self-attention neural network architecture with dilated 1D convolutions for speech recognition, achieving state-of-the-art results on LibriSpeech.

Contribution

It proposes a novel multi-stream self-attention model with dilated convolutions to better handle correlated speech frames, improving speech recognition accuracy.

Findings

Achieved 2.2% WER on LibriSpeech test-clean dataset.

Outperforms previous models on speech recognition benchmarks.

Demonstrates efficiency of multi-resolution attention in speech tasks.

Abstract

Self-attention has been a huge success for many downstream tasks in NLP, which led to exploration of applying self-attention to speech problems as well. The efficacy of self-attention in speech applications, however, seems not fully blown yet since it is challenging to handle highly correlated speech frames in the context of self-attention. In this paper we propose a new neural network model architecture, namely multi-stream self-attention, to address the issue thus make the self-attention mechanism more effective for speech recognition. The proposed model architecture consists of parallel streams of self-attention encoders, and each stream has layers of 1D convolutions with dilated kernels whose dilation rates are unique given stream, followed by a self-attention layer. The self-attention mechanism in each stream pays attention to only one resolution of input speech frames and the attentive computation can be more efficient. In a later stage, outputs from all the streams are concatenated then linearly projected to the final embedding. By stacking the proposed multi-stream self-attention encoder blocks and rescoring the resultant lattices with neural network language models, we achieve the word error rate of 2.2% on the test-clean dataset of the LibriSpeech corpus, the best number reported thus far on the dataset.