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

Researchers have developed a speech-driven end-to-end model for discriminating among Chinese dialects, combining MFCC features with word-level embeddings via a CNN. The approach outperforms state-of-the-art methods on two benchmark corpora, addressing the limitations of text-driven language discrimination.

arXiv:2606.18584v1 Announce Type: new 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.