RESEARCH

SPEECH & AUDIO

Who Needs Words? Lexicon-Free Speech Recognition

September 15, 2019

Abstract

Lexicon-free speech recognition naturally deals with the problem of out-of-vocabulary (OOV) words. In this paper, we show that character-based language models (LM) can perform as well as word-based LMs for speech recognition, in word error rates (WER), even without restricting the decoding to a lexicon. We study character-based LMs and show that convolutional LMs can effectively leverage large (character) contexts, which is key for good speech recognition performance downstream. We specifically show that the lexicon-free decoding performance (WER) on utterances with OOV words using character-based LMs is better than lexicon-based decoding, with character or word-based LMs.

Download the Paper

Related Publications

July 28, 2019

SPEECH & AUDIO

COMPUTER VISION

Learning to Optimize Halide with Tree Search and Random Programs | Facebook AI Research

Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, Tzu-Mao Li, Michaël Gharbi, Benoit Steiner, Steven Johnson, Kayvon Fatahalian, Frédo Durand, Jonathan Ragan-Kelley

July 28, 2019

September 15, 2019

SPEECH & AUDIO

Who Needs Words? Lexicon-Free Speech Recognition | Facebook AI Research

Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert

September 15, 2019

December 04, 2018

SPEECH & AUDIO

Non-Adversarial Mapping with VAEs | Facebook AI Research

Yedid Hoshen

December 04, 2018

May 01, 2019

SPEECH & AUDIO

Learning graphs from data: A signal representation perspective | Facebook AI Research

Xiaowen Dong, Dorina Thanou, Michael Rabbat, Pascal Frossard

May 01, 2019

Related Work

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.