Comparison of HMM and DTW methods in automatic recognition of pathological phoneme pronunciation

Robert Wielgat, Tomasz P. Zielinski, Pawel Swietojanski, Piotr Zoladz, Daniel Król, Tomasz Wozniak, Stanislaw Grabias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the paper recently proposed Human Factor Cepstral Coefficients (HFCC) are used to automatic recognition of pathological phoneme pronunciation in speech of impaired children and efficiency of this approach is compared to application of the standard Mel-Frequency Cepstral Coefficients (MFCC) as a feature vector. Both dynamic time warping (DTW), working on whole words or embedded phoneme patterns, and hidden Markov models (HMM) are used as classifiers in the presented research. Obtained results demonstrate superiority of combining HFCC features and modified phoneme-based DTW classifier.
Original languageEnglish
Title of host publicationINTERSPEECH 2007, 8th Annual Conference of the International Speech Communication Association, Antwerp, Belgium, August 27-31, 2007
PublisherISCA
Pages1705-1708
Number of pages4
Publication statusPublished - 2007
Event8th Annual Conference of the International Speech Communication Association - Antwerp, Belgium
Duration: 27 Aug 200731 Aug 2007

Conference

Conference8th Annual Conference of the International Speech Communication Association
Country/TerritoryBelgium
CityAntwerp
Period27/08/0731/08/07

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