Reducing language context confusion for end-to-end code-switching automatic speech recognition

• URL: http://arxiv.org/abs/2201.12155v1
• Date: Fri, 28 Jan 2022 14:39:29 GMT
• Title: Reducing language context confusion for end-to-end code-switching automatic speech recognition
• Authors: Shuai Zhang, Jiangyan Yi, Zhengkun Tian, Jianhua Tao, Yu Ting Yeung, Liqun Deng
• Abstract summary: We propose a language-related attention mechanism to reduce multilingual context confusion for the E2E code-switching ASR model. By calculating the respective attention of multiple languages, our method can efficiently transfer language knowledge from rich monolingual data.
• Score: 50.89821865949395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code-switching is about dealing with alternative languages in the communication process. Training end-to-end (E2E) automatic speech recognition (ASR) systems for code-switching is known to be a challenging problem because of the lack of data compounded by the increased language context confusion due to the presence of more than one language. In this paper, we propose a language-related attention mechanism to reduce multilingual context confusion for the E2E code-switching ASR model based on the Equivalence Constraint Theory (EC). The linguistic theory requires that any monolingual fragment that occurs in the code-switching sentence must occur in one of the monolingual sentences. It establishes a bridge between monolingual data and code-switching data. By calculating the respective attention of multiple languages, our method can efficiently transfer language knowledge from rich monolingual data. We evaluate our method on ASRU 2019 Mandarin-English code-switching challenge dataset. Compared with the baseline model, the proposed method achieves 11.37% relative mix error rate reduction.

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