Chinese accent detection research based on RASTA - PLP algorithm

Accent is a critical important component of spoken communication, which plays a very important role in spoken communication. In this paper, we conduct accent by using RASTA - PLP algorithm to extract short-time spectrum features of each speech segment based on sub-segment splicing information. We build short-time spectrum feature sets based on RASTA - PLP algorithm. And we choose NaiveBayes classifier to model the feature sets. NaiveBayes is to choose the class with maximum posteriori probability as the object´s class. This classification method makes full use of the related phonetic features of speech segment. Based on short-time spectrum of RASTA - PLP feature sets respectively achieve 80.8% accent detection accuracy on ASCCD and on ASCCD (NOISEX92-white). The experimental results indicate that based on sub-segment splicing feature structured method of RASTA - PLP can be used in Chinese accent detection study. RASTA-PLP algorithm is robust on ASSCD and on ASSCD (NOISEX92-white).