Facebook Opens Up Speech Dataset for AI Speech Recognition
Facebook AI has released a new speech dataset that could help improve the accuracy of speech recognition technology for a wider range of languages. The dataset, called Multilingual LibriSpeech (MLS), contains over 50,000 hours of audio across eight languages including English, German, Dutch, French, Spanish, Italian, Portuguese, and Polish.
The MLS dataset was created by leveraging LibriVox audiobook data. LibriVox is a free online library of audiobooks that are read aloud by volunteers. The audiobooks in the MLS dataset were transcribed, and the audio and transcriptions were then aligned. This allows researchers to use the dataset to train speech recognition models that can accurately transcribe speech in a variety of languages reported tech guru author, J Dean.
In addition to the MLS dataset, Facebook AI has also released a new unsupervised speech recognition model called wav2vec-U. Wav2vec-U is a self-supervised learning model that can be trained on unlabeled speech data. This makes it possible to train speech recognition models for languages where there is limited or no labeled data.
The combination of the MLS dataset and the wav2vec-U model has the potential to significantly improve the accuracy of speech recognition technology for a wider range of languages. This could have a number of benefits, including making it easier for people to use voice-activated devices and services in their native language.
Impact of the MLS dataset
The MLS dataset is expected to have a significant impact on the field of speech recognition. The dataset is more than 10 times larger than the previous largest multilingual speech dataset, and it covers a wider range of languages. This will allow researchers to train speech recognition models that are more accurate and robust.
The MLS dataset is also expected to help make speech recognition technology more accessible to people who speak languages that are not widely spoken. This is because the dataset includes audio from a variety of dialects and accents. This means that speech recognition models trained on the MLS dataset will be able to better understand people who speak different ways.
Impact of the wav2vec-U model
The wav2vec-U model is also expected to have a significant impact on the field of speech recognition. The model is able to learn the underlying structure of speech without any labeled data. This means that it can be trained on a variety of languages, even if there is limited or no labeled data available.
The wav2vec-U model is also more accurate than previous unsupervised speech recognition models. This is because the model is able to learn more complex features of speech. As a result, the model is able to better understand speech that is noisy or distorted.
Conclusion
The combination of the MLS dataset and the wav2vec-U model has the potential to significantly improve the accuracy of speech recognition technology for a wider range of languages. This could have a number of benefits, including making it easier for people to use voice-activated devices and services in their native language.
The MLS dataset and the wav2vec-U model are both open source, which means that they can be used by anyone. This will help to accelerate the development of new speech recognition technologies and make them more accessible to people around the world.
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