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Maintaining the diversity of the world’s languages ​​through AI

Maintaining the diversity of the world’s languages ​​through AI


Supports thousands of languages

Many of the world’s languages ​​are on the verge of extinction, and the limitations of current speech recognition and generation technology will only accelerate this trend. We want people to be able to access information and use their devices in the language of their choice, and today we’re announcing a set of artificial intelligence (AI) models that can help you do just that.

Massive Multilingual Speech (MMS) model expands text-to-speech and speech-to-text technology from about 100 languages ​​to over 1,100, more than 10x before, and identifies more than 4,000 spoken languages, 40x more can also do. Before.

There are also many use cases for voice technology, from virtual and augmented reality technologies to messaging services that can be used in the language of your choice and understand everyone’s voice.

We open sourced our models and code so others in the research community can build on our research, preserve the world’s languages, and bring the world closer together.

our approach

Collecting speech data for thousands of languages ​​was our first challenge, as the largest existing speech dataset covers at most 100 languages. To overcome this, we turned to religious documents such as the Bible, which have been translated into many different languages ​​and whose translations have been extensively studied for text-based language translation research.

These translations have published audio recordings of people reading these texts in different languages. As part of the MMS project, we created datasets of New Testament readings in over 1,100 languages. This provided an average of 32 hours of data per language.

Considering unlabeled recordings of various other Christian religious readings increased the number of available languages ​​to over 4,000. Although this data is domain specific and is often read by male speakers, our analysis shows that the model performs equally well for male and female voices. It has been. Also, although the content of the audio recordings is religious, our analysis shows that this does not bias the model to produce more religious language.

from now on

In the future, we hope to extend MMS coverage to support more languages, as well as tackle the challenge of handling dialects, which is often difficult with existing speech technologies. .

Learn more about MMS here.




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