Weak-Attention Suppression For Transformer Based Speech Recognition



Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR). However, adjacent acoustic units (i.e., frames) are highly correlated, and long-distance dependencies between them are weak, unlike text units. It suggests that ASR will likely benefit from sparse and localized attention. In this paper, we propose Weak-Attention Suppression (WAS), a method that dynamically induces sparsity in attention probabilities. We demonstrate that WAS leads to consistent Word Error Rate (WER) improvement over strong transformer baselines. On the widely used LibriSpeech benchmark, our proposed method reduced WER by 10% on test-clean and 5% on test-other for streamable transformers, resulting in a new state-of-the-art among streaming models. Further analysis shows that WAS learns to suppress attention of non-critical and redundant continuous acoustic frames, and is more likely to suppress past frames rather than future ones. It indicates the importance of lookahead in attention-based ASR models.

Related Publications

All Publications

SIGGRAPH - August 9, 2021

Control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports

Jungdam Won, Deepak Gopinath, Jessica Hodgins

CVPR - June 20, 2021

Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos

Yanghao Li, Tushar Nagarajan, Bo Xiong, Kristen Grauman

ICML - July 18, 2021

Align, then memorise: the dynamics of learning with feedback alignment

Maria Refinetti, St├ęphane d'Ascoli, Ruben Ohana, Sebastian Goldt

CVPR - June 19, 2021

Intentonomy: a Dataset and Study towards Human Intent Understanding

Menglin Jia, Zuxuan Wu, Austin Reiter, Claire Cardie, Serge Belongie, Ser-Nam Lim

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy