NLP

Unsupervised Speech Recognition

October 25, 2021

Abstract

Despite rapid progress in the recent past, current speech recognition systems still require labeled training data which limits this technology to a small fraction of the languages spoken around the globe. This paper describes wav2vec-U, short for wav2vec Unsupervised, a method to train speech recognition models without any labeled data. We leverage self-supervised speech representations to segment unlabeled audio and learn a mapping from these representations to phonemes via adversarial training. The right representations are key to the success of our method. Compared to the best previous unsupervised work, wav2vec-U reduces the phone error rate on the TIMIT benchmark from 26.1 to 11.3. On the larger English Librispeech benchmark, wav2vec-U achieves a word error rate of 5.9 on test-other, rivaling some of the best published systems trained on 960 hours of labeled data from only two years ago. We also experiment on nine other languages, including low-resource languages such as Kyrgyz, Swahili and Tatar. The code will be open sourced.

Download the Paper

AUTHORS

Written by

Alexei Baevski

Wei-Ning Hsu

Alexis Conneau

Michael Auli

Publisher

NeurIPS

Related Publications

February 21, 2024

INTEGRITY

NLP

Watermarking Makes Language Models Radioactive

Tom Sander, Pierre Fernandez, Alain Durmus, Matthijs Douze, Teddy Furon

February 21, 2024

December 07, 2023

CONVERSATIONAL AI

NLP

Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Davide Testuggine, Madian Khabsa

December 07, 2023

December 06, 2023

NLP

Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings

Mattia Atzeni, Mike Plekhanov, Frederic Dreyer, Nora Kassner, Simone Merello, Louis Martin, Nicola Cancedda

December 06, 2023

December 04, 2023

NLP

PATHFINDER: Guided Search over Multi-Step Reasoning Paths

Olga Golovneva, Sean O'Brien, Ram Pasunuru, Tianlu Wang, Luke Zettlemoyer, Maryam Fazel-Zarandi, Asli Celikyilmaz

December 04, 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.