Improved Language Identification Through Cross-Lingual Self-Supervised Learning

• URL: http://arxiv.org/abs/2107.04082v1
• Date: Thu, 8 Jul 2021 19:37:06 GMT
• Title: Improved Language Identification Through Cross-Lingual Self-Supervised Learning
• Authors: Andros Tjandra, Diptanu Gon Choudhury, Frank Zhang, Kritika Singh, Alexei Baevski, Assaf Sela, Yatharth Saraf, Michael Auli
• Abstract summary: We extend previous self-supervised work on language identification by experimenting with pre-trained models. Results on a 25 languages setup show that with only 10 minutes of labeled data per language, a cross-lingually pre-trained model can achieve over 93% accuracy.
• Score: 37.32193095549614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language identification greatly impacts the success of downstream tasks such as automatic speech recognition. Recently, self-supervised speech representations learned by wav2vec 2.0 have been shown to be very effective for a range of speech tasks. We extend previous self-supervised work on language identification by experimenting with pre-trained models which were learned on real-world unconstrained speech in multiple languages and not just on English. We show that models pre-trained on many languages perform better and enable language identification systems that require very little labeled data to perform well. Results on a 25 languages setup show that with only 10 minutes of labeled data per language, a cross-lingually pre-trained model can achieve over 93% accuracy.

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