Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Effective Machine Learning Needs Leadership — Not AI Hype
 Originally published in BigThink, Feb 12, 2024.  Excerpted from The...
Today’s AI Won’t Radically Transform Society, But It’s Already Reshaping Business
 Originally published in Fast Company, Jan 5, 2024. Eric...
A University Curriculum Supplement to Teach a Business Framework for ML Deployment
    In 2023, as a visiting analytics professor...
The AI Playbook: Providing Important Reminders to Data Professionals
 Originally published in DATAVERSITY. This article reviews the new...
SHARE THIS:

9 months ago
Introducing Speech-to-Text, Text-to-Speech, and More for 1,100+ Languages

 
Originally published in Meta AI, May 22, 2023.

Equipping machines with the ability to recognize and produce speech can make information accessible to many more people, including those who rely entirely on voice to access information. However, producing good-quality machine learning models for these tasks requires large amounts of labeled data — in this case, many thousands of hours of audio, along with transcriptions. For most languages, this data simply does not exist. For example, existing speech recognition models only cover approximately 100 languages — a fraction of the 7,000+ known languages spoken on the planet. Even more concerning, nearly half of these languages are in danger of disappearing in our lifetime.

In the Massively Multilingual Speech (MMS) project, we overcome some of these challenges by combining wav2vec 2.0, our pioneering work in self-supervised learning, and a new dataset that provides labeled data for over 1,100 languages and unlabeled data for nearly 4,000 languages. Some of these, such as the Tatuyo language, have only a few hundred speakers, and for most of these languages, no prior speech technology exists. Our results show that the Massively Multilingual Speech models outperform existing models and cover 10 times as many languages. Meta is focused on multilinguality in general: For text, the NLLB project scaled multilingual translation to 200 languages, and the Massively Multilingual Speech project scales speech technology to many more languages.

To continue reading this article, click here.

11 thoughts on “Introducing Speech-to-Text, Text-to-Speech, and More for 1,100+ Languages

Leave a Reply