Blind Location of Phonetic Boundaries

This contribution addresses the location of phonetic boundaries (LPB) for Czech phonetic categories. A novel method based on discriminant function and Bayesian change-point detectors (BCD) is suggested and tested for synthetic and real speech; the consistency and strength of the method was confirmed by experiment. The LPB process for finding significant boundaries consists of four steps: pitch-synchronous segmentation, signal parameterization using Bayesian evidence with polynomial and autoregressive models, discriminant function evaluation and BCD application. The proper boundary location is in average greater than 75% for continuous speech. The error in the time-location of boundaries less than 6 ms can be achieved for affricates/vowels, burst/vowel and silence/most of the phonetic categories. INTRODUCTION The detection, estimation and location of speech discontinuities (changepoints) has been intensively studied for several decades. Many methods for speech segmentation based on various characteristics have been developed. The most widely used segmentation principles are the likelihood a nalysis [1], the hidden Markov models (HMM) [2], the Bayesian approach with a HMM method [3], the combination of the Bayesian approach with rules [4], and discrimination analysis [5]. This contribution deals with the possibility of using the combination of discrimination analysis with Bayesian evidence (BE) [6], and Bayesian changepoint detectors (BCD) [7]. The main motivation for this work was text-to-speech inventory acquisition. This approach requires the training of discriminant functions [9] for chosen speech classes, but it is not as extensive as the model training, which is required if HMM or neural nets are used. The LPB process consists of four steps: modified pitch-synchronous segmentation [11], followed by a signal parameterization using BE. Then a suitable discriminant function used for the segment concatenation is applied. Finally two types of BCDs are used to locate the final boundaries. Signal parameters are estimated by an algorithm of Bayesian model order selection and show us to what degree of accuracy it is possible to describe one pitch period using polynomial [8] or autoregressive models [6], [7], [8]. The parameter vector v of one pitch period segment is then given by BEs)] 8 () 1 () 4 () 0 ([ AR AR PM PM = v. The discriminant function [9] is determined for each vector v in (1). Two possible classes must be used for segment concatenation. The decision strategy is to associate the current f rame with the past frame if there is the same class in both of the neighboring frames. No …