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.
AUTHORS
Publisher
Interspeech
Related Publications
April 14, 2024
SPEECH & AUDIO
NLP
CoLLD: Contrastive Layer-to-Layer Distillation for Compressing Multilingual Pre-Trained Speech Encoders
Heng-Jui Chang, Ning Dong (AI), Ruslan Mavlyutov, Sravya Popuri, Andy Chung
April 14, 2024
December 11, 2023
SPEECH & AUDIO
Audiobox: Unified Audio Generation with Natural Language Prompts
Wei-Ning Hsu, Akinniyi Akinyemi, Alice Rakotoarison, Andros Tjandra, Apoorv Vyas, Baishan Guo, Bapi Akula, Bowen Shi, Brian Ellis, Ivan Cruz, Jeff Wang, Jiemin Zhang, Mary Williamson, Matt Le, Rashel Moritz, Robbie Adkins, William Ngan, Xinyue Zhang, Yael Yungster, Yi-Chiao Wu
December 11, 2023
November 30, 2023
SPEECH & AUDIO
NLP
Efficient Monotonic Multihead Attention
Xutai Ma, Anna Sun, Siqi Ouyang, Hirofumi Inaguma, Paden Tomasello
November 30, 2023
November 30, 2023
SPEECH & AUDIO
NLP
Seamless: Multilingual Expressive and Streaming Speech Translation
Seamless Communication, Loïc Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, John Hoffman, Min-Jae Hwang, Hirofumi Inaguma, Christopher Klaiber, Ilia Kulikov, Pengwei Li, Daniel Licht, Jean Maillard, Ruslan Mavlyutov, Alice Rakotoarison, Kaushik Ram Sadagopan, Abinesh Ramakrishnan, Tuan Tran, Guillaume Wenzek, Yilin Yang, Ethan Ye, Ivan Evtimov, Pierre Fernandez, Cynthia Gao, Prangthip Hansanti, Elahe Kalbassi, Amanda Kallet, Artyom Kozhevnikov, Gabriel Mejia Gonzalez, Robin San Roman, Christophe Touret, Corinne Wong, Carleigh Wood, Bokai Yu, Pierre Andrews, Can Balioglu, Peng-Jen Chen, Marta R. Costa-jussà, Maha Elbayad, Hongyu Gong, Francisco Guzmán, Kevin Heffernan, Somya Jain, Justine Kao, Ann Lee, Xutai Ma, Alexandre Mourachko, Benjamin Peloquin, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Anna Sun, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang, Mary Williamson
November 30, 2023
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.
Product experiences
Foundational models
Product experiences
Latest news
Foundational models
Meta © 2024