Use of PLP Cepstral Features for Phonetic Segmentation

Abstract

Phonetic segmentation can find its potential application for Text-to-Speech (TTS) synthesis and Automatic Speech Recognition (ASR) systems. In this paper, we propose use of Perceptual Linear Prediction Cepstral Coefficients (PLPCC) feature for phonetic segmentation task. To detect phonetic boundaries, we used spectral transition measure (STM). Using proposed approach, we achieve 85 % (i.e., 3 % better than state-of-the art Mel-frequency Cepstral Coefficients (MFCC) for 20 ms agreement duration) accuracy and 15 % over-segmentation rate (i.e., 8 % less than MFCC) for automatic boundary detection of 2, 34, 925 phone boundaries corresponding 630 speakers of entire TIMIT database.