Speech-Driven End-to-End Language Discrimination towards Chinese Dialects

• URL: http://arxiv.org/abs/2606.18584v1
• Date: Wed, 17 Jun 2026 01:23:58 GMT
• Title: Speech-Driven End-to-End Language Discrimination towards Chinese Dialects
• Abstract summary: We explore the effectiveness of speech-driven features towards language discrimination among Chinese dialects.<n>We design an end-to-end speech recognition model based on HMM-DNN to predict Chinese dialect words.<n> Evaluation of two benchmark Chinese dialect corpora shows the appropriateness and effectiveness of the proposed speech-driven approach.
• Score: 17.68215217103399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language discrimination among similar languages, varieties, and dialects is a challenging natural language processing task. The traditional text-driven focus leads to poor results. In this paper, we explore the effectiveness of speech-driven features towards language discrimination among Chinese dialects. First, we systematically explore the appropriateness of speech-driven MFCC features towards CNN-based language discrimination. Then, we design an end-to-end speech recognition model based on HMM-DNN to predict Chinese dialect words. We adopt attention to extract the discriminative words related to different Chinese dialects. Finally, through a CNN, we combine the word-level embedding and the MFCC-based features. Evaluation of two benchmark Chinese dialect corpora shows the appropriateness and effectiveness of the proposed speech-driven approach to fine-grained Chinese dialect discrimination compared to the state-of-the-art methods.

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