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Leveraging BERT to Improve Spoken Language Identification of Code-Switching Speech
- Source :
- International Journal of Asian Language Processing; March 2024, Vol. 34 Issue: 1
- Publication Year :
- 2024
-
Abstract
- Language identification (LID) involves automatically determining the language being spoken in a given segment. In code-switching speech, there are rapid switches between two or more languages within a single conversation. Despite LID attaining high accuracy on medium or long utterances, the performance on short utterances of code-switching speech is currently unsatisfactory. We propose a bidirectional encoder representation from transformers (BERT)-based LID system (BERT-LID) to enhance LID performance, especially for short-duration code-switching speech segments. The original BERT model is expanded by incorporating phonetic posterior grams (PPGs) extracted from a front-end phone recognizer as input. Then it is followed by the deployment of an optimal deep classifier for LID. Our BERT-LID model demonstrates a significant improvement of approximately 6.5% in accuracy for long-segment identification and 19.9% for short-segment identification, thereby demonstrating its effectiveness in code-switching speech LID tasks.
Details
- Language :
- English
- ISSN :
- 27175545 and 2424791X
- Volume :
- 34
- Issue :
- 1
- Database :
- Supplemental Index
- Journal :
- International Journal of Asian Language Processing
- Publication Type :
- Periodical
- Accession number :
- ejs67077218
- Full Text :
- https://doi.org/10.1142/S2717554524500036