RESEARCH
SPEECH & AUDIO
Weak-Attention Suppression For Transformer Based Speech Recognition
August 13, 2020
Abstract
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.
AUTHORS
Written by
Ching-Feng Yeh
Chunyang Wu
Duc Le
Frank Zhang
Mike Seltzer
Yongqiang Wang
Publisher
Interspeech
Related Publications
May 12, 2026
HUMAN & MACHINE INTELLIGENCE
RESEARCH
NeuralSet: A High-Performing Python Package for Neuro-AI
Corentin Bel, Linnea Evanson, Julien Gadonneix, Andrea Santos Revilla, Mingfang (Lucy) Zhang, Julie Bonnaire, Charlotte Caucheteux, Alexandre Défossez, Théo Desbordes, Pablo Diego-Simón, Shubh Khanna, Juliette Millet, Pierre Orhan, Saarang Panchavati, Antoine Ratouchniak, Alexis Thual, Hubert Jacob Banville, Jarod Levy, Jean Remi King, Josephine Raugel, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Sophia Houhamdi, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit
May 12, 2026
May 06, 2026
HUMAN & MACHINE INTELLIGENCE
RESEARCH
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
Saarang Panchavati, Antoine Ratouchniak, Mingfang (Lucy) Zhang, Elisa Cascardi, Hubert Banville, Jarod Levy, Jean-Rémi King, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit
May 06, 2026
April 16, 2026
RESEARCH
AIRA₂: Overcoming Bottlenecks in AI Research Agents
Nicola Cancedda, Pontus Stenetorp, Alexis Audran-Reiss, Alisia Lupidi, Anton Protopopov, Bassel Al Omari, Carole-Jean Wu, Derek Dunfield, Despoina Magka, Edan Toledo, Hela Momand, Ishita Mediratta, Jakob Foerster, Jean-Christophe Gagnon-Audet, Karen Hambardzumyan, Kelvin Niu, Martin Josifoski, Michael Kuchnik, Michael Shvartsman, Nicolas Baldwin, Parth Pathak, Rishi Hazra, Tatiana Shavrina, Thomas Simon Foster, Yoram Bachrach
April 16, 2026
March 17, 2026
RESEARCH
NLP
Omnilingual MT: Machine Translation for 1,600 Languages
Omnilingual MT Team, Niyati Bafna, Ioannis Tsiamas, Mark Duppenthaler, Albert Ventayol-Boada, Alexandre Mourachko, Andrea Caciolai, Arina Turkatenko, Artyom Kozhevnikov, Belen Alastruey, Charles-Eric Saint-James, Chierh CHENG, Christophe Ropers, Cynthia Gao, David Dale, Edan Toledo, Eduardo Sánchez, Gabriel Mejia Gonzalez, Holger Schwenk, Jean Maillard, Joe Chuang, João Maria Janeiro, Kevin Heffernan, Marta R. Costa-jussa, Mary Williamson, Nate Ekberg, Paul-Ambroise Duquenne, Pere Lluís Huguet Cabot, Rashel Moritz, Shireen Yates, Surya Parimi
March 17, 2026
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Our approach
Latest news
Foundational models
Meta © 2026