Abstract
Accent is a critically important component of spoken communication, and plays a very important role in spoken communication. In this paper, we conduct accent by using MFCC algorithm and RASTA - PLP algorithm to extract short-time spectrum features of each speech segment based on features structured information. We build short-time spectrum feature sets based on MFCC algorithm and RASTA - PLP algorithm. And we choose NaiveBayes classifier to model the two 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 MFCC feature set and short-time spectrum of RASTA - PLP feature set respectively achieve 82.1% and 80.8% accent detection accuracy on ASCCD. The experimental results indicate that based on sub-segment splicing feature structured method of MFCC and sub-segment splicing feature structured method of RASTA - PLP can be used in Chinese accent detection study.
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