Scaling Speech Technology to 1,000+ Languages

May 22, 2023

Abstract

Expanding the language coverage of speech technology has the potential to improve access to information for many more people. However, current speech technology is restricted to about one hundred languages which is a small fraction of the over 7,000 languages spoken around the world. The Massively Multilingual Speech (MMS) project increases the number of supported languages by 10-40x, depending on the task. The main ingredients are a new dataset based on readings of publicly available religious texts and effectively leveraging self-supervised learning. We built pre-trained wav2vec 2.0 models covering 1,406 languages, a single multilingual automatic speech recognition model for 1,107 languages, speech synthesis models for the same number of languages, as well as a language identification model for 4,017 languages. Experiments show that our multilingual speech recognition model more than halves the word error rate of Whisper on 54 languages of the FLEURS benchmark while being trained on a small fraction of the labeled data.

Download the Paper

AUTHORS

Written by

Vineel Pratap

Andros Tjandra

Bowen Shi

Paden Tomasello

Arun Babu

Sayani Kundu

Ali Elkahky

Apoorv Vyas

Maryam Fazel-Zarandi

Alexei Baevski

Wei-Ning Hsu

Alexis Conneau

Michael Auli

Publisher

NeurIPS

Related Publications

October 19, 2025

RESEARCH

NLP

Controlling Multimodal LLMs via Reward-guided Decoding

Oscar Mañas, Pierluca D'Oro, Koustuv Sinha, Adriana Romero Soriano, Michal Drozdzal, Aishwarya Agrawal

October 19, 2025

Read the Paper

October 13, 2025

REINFORCEMENT LEARNING

RESEARCH

SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models

Chenyu Wang, Paria Rashidinejad, DiJia Su, Song Jiang, Sid Wang, Siyan Zhao, Cai Zhou, Shannon Zejiang Shen, Feiyu Chen, Tommi Jaakkola, Yuandong Tian, Bo Liu

October 13, 2025

Read the Paper

September 24, 2025

RESEARCH

NLP

CWM: An Open-Weights LLM for Research on Code Generation with World Models

Jade Copet, Quentin Carbonneaux, Gal Cohen, Jonas Gehring, Jacob Kahn, Jannik Kossen, Felix Kreuk, Emily McMilin, Michel Meyer, Yuxiang Wei, David Zhang, Kunhao Zheng, Jordi Armengol Estape, Pedram Bashiri, Maximilian Beck, Pierre Chambon, Abhishek Charnalia, Chris Cummins, Juliette Decugis, Zacharias Fisches, François Fleuret, Fabian Gloeckle, Alex Gu, Michael Hassid, Daniel Haziza, Badr Youbi Idrissi, Christian Keller, Rahul Kindi, Hugh Leather, Gallil Maimon, Aram Markosyan, Francisco Massa, Pierre-Emmanuel Mazaré, Vegard Mella, Naila Murray, Keyur Muzumdar, Peter O'Hearn, Matteo Pagliardini, Dmitrii Pedchenko, Tal Remez, Volker Seeker, Marco Selvi, Oren Sultan, Sida Wang, Luca Wehrstedt, Ori Yoran, Lingming Zhang, Taco Cohen, Yossi Adi, Gabriel Synnaeve

September 24, 2025

Read the Paper

September 24, 2025

RESEARCH

NLP

Code World Model Preparedness Report

Daniel Song, Peter Ney, Cristina Menghini, Faizan Ahmad, Aidan Boyd, Nathaniel Li, Ziwen Han, Jean-Christophe Testud, Saisuke Okabayashi, Maeve Ryan, Jinpeng Miao, Hamza Kwisaba, Felix Binder, Spencer Whitman, Jim Gust, Esteban Arcaute, Dhaval Kapil, Jacob Kahn, Ayaz Minhas, Tristan Goodman, Lauren Deason, Alexander Vaughan, Shengjia Zhao, Summer Yue