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

2026-06-17 01:23:58
Fan Xu, Jian Luo, MingWen Wang, GuoDong Zhou
arXiv_CL
             
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.

Abstract (translated)

URL

https://arxiv.org/abs/2606.18584

PDF

https://arxiv.org/pdf/2606.18584.pdf

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