Phonetic segmentation: unsupervised approach

Abstract

Phonetic segmentation can find its potential application for Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) Synthesis systems. In this thesis, we propose use of different spectral features viz., Mel Frequency Cepstral Coefficients (MFCC), Cochlear Filter Cepstral Coefficients (CFCC) and Perceptual Linear Prediction Cepstral Coefficients (PLPCC)-based features to detect spectral transition measure (STM) for automatic phonetic boundaries. We propose a new unsupervised algorithm by combining evidences from state-of-the-art Mel Frequency Cepstral Coefficients (MFCC) and proposed CFCC to improve the accuracy in automatic phonetic boundaries detection process. Using proposed fusion-based approach, we achieve 90 % (i.e., 8 % better than MFCC-based STM alone for 20 ms tolerance interval) accuracy for automatic boundary detection of entire TIMIT database. Using proposed PLPCC-base STM approach, we achieve 85 % (i.e., 3 % better than state-of the art Mel- frequency Cepstral Coefficients (MFCC)-based STM for 20 ms tolerance interval) accuracy and 15 % over-segmentation rate (i.e., 8 % less than MFCC-based STM) for automatic boundary detection of 2, 34, 925 phone boundaries corresponding 630 speakers of entire TIMIT database. The second part of the thesis is focusing on development of various applications using automatically segmented and labeled boundaries.