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
December 18, 2025
RESEARCH
COMPUTER VISION
Pixel Seal: Adversarial-only training for invisible image and video watermarking
Tomáš Souček, Pierre Fernandez, Hady Elsahar, Sylvestre Rebuffi, Valeriu Lacatusu, Tuan Tran, Tom Sander, Alexandre Mourachko
December 18, 2025
December 16, 2025
SPEECH & AUDIO
COMPUTER VISION
SAM Audio: Segment Anything in Audio
Bowen Shi, Andros Tjandra, John Hoffman, Helin Wang, Yi-Chiao Wu, Luya Gao, Julius Richter, Matt Le, Apoorv Vyas, Sanyuan Chen, Christoph Feichtenhofer, Piotr Dollar, Wei-Ning Hsu, Ann Lee
December 16, 2025
December 16, 2025
SPEECH & AUDIO
COMPUTER VISION
Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning
Apoorv Vyas, Heng-Jui Chang, Cheng-Fu Yang, Bernie Huang, Luya Gao, Julius Richter, Sanyuan Chen, Matt Le, Piotr Dollar, Christoph Feichtenhofer, Ann Lee, Wei-Ning Hsu
December 16, 2025
November 19, 2025
RESEARCH
COMPUTER VISION
SAM 3: Segment Anything with Concepts
Nicolas Carion, Laura Gustafson, Yuan-Ting Hu, Shoubhik Debnath, Ronghang Hu, Didac Suris Coll-Vinent, Chaitanya Ryali, Kalyan Vasudev Alwala, Haitham Khedr, Andrew Huang, Jie Lei, Tengyu Ma, Baishan Guo, Arpit Kalla, Markus Marks, Joseph Greer, Meng Wang, Peize Sun, Roman Rädle, Triantafyllos Afouras, Effrosyni Mavroudi, Katherine Xu, Tsung-Han Wu, Yu Zhou, Liliane Momeni, Rishi Hazra, Shuangrui Ding, Sagar Vaze, Francois Porcher, Feng Li, Siyuan Li, Aishwarya Kamath, Ho Kei Cheng, Piotr Dollar, Nikhila Ravi, Kate Saenko, Pengchuan Zhang, Christoph Feichtenhofer
November 19, 2025
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