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BERT-LID: Leveraging BERT to Improve Spoken Language Identification

conference contribution
posted on 2023-02-12, 20:32 authored by Yuting Nie, Junhong ZhaoJunhong Zhao, Wei-Qiang Zhang, Jinfeng Bai
Language identification is the task of automatically determining the identity of a language conveyed by a spoken segment. It has a profound impact on the multilingual interoperability of an intelligent speech system. Despite language identification attaining high accuracy on medium or long utterances(>3s), the performance on short utterances (<=1s) is still far from satisfactory. We propose a BERT-based language identification system (BERT-LID) to improve language identification performance, especially on short-duration speech segments. We extend the original BERT model by taking the phonetic posteriorgrams (PPG) derived from the front-end phone recognizer as input. Then we deployed the optimal deep classifier followed by it for language identification. Our BERT-LID model can improve the baseline accuracy by about 6.5% on long-segment identification and 19.9% on short-segment identification, demonstrating our BERT-LID's effectiveness to language identification


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Nie, Y., Zhao, J., Zhang, W. -Q. & Bai, J. (2022, October). BERT-LID: Leveraging BERT to Improve Spoken Language Identification.

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