Entropy-Based Analysis of the Prosodic Features of Chinese Dialects

In this paper, a novel approach is proposed to analyze prosodic features of four Chinese dialects: Wu, Cantonese, Min and Mandarin. The ultimate goal is to exploit these features in the task of automatic spoken language identification. Two entropy-based evaluation metrics are formulated to address the problems of data sparseness and lack of speakers. Different prosody-related acoustic features and their combinations are evaluated. FO, FO gradient and intensity are found to contain the most language-related information. Maximum language-related information are observed in multi-dimensional N-gram features with FO, FO gradient and syllable position in sentence. There are also some uncertain results that reveal the limitations of the proposed metrics.