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

The paper presents a novel end-to-end speech recognition model for language discrimination among Chinese dialects, utilizing speech-driven MFCC features and a CNN architecture. It incorporates an HMM-DNN framework with attention mechanisms to enhance word-level discrimination. The approach demonstrates improved performance on benchmark Chinese dialect corpora, highlighting its significance for practitioners focusing on fine-grained language discrimination tasks in NLP.