Title page for ETD etd-02082010-174617

Type of Document Master's Thesis
Author Baghdasaryan, Areg Gagik
Author's Email Address aregb@vt.edu
URN etd-02082010-174617
Title Automatic Phoneme Recognition with Segmental Hidden Markov Models
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Beex, A. A. Louis Committee Chair
da Silva, Claudio R. C. M. Committee Member
Wyatt, Christopher L. Committee Member
  • Viterbi
  • Baum Welch
  • Hidden Markov Model
  • Segmental HMM
  • Cluster
  • Speech
  • Speaker
Date of Defense 2010-01-27
Availability unrestricted
A speaker independent continuous speech phoneme recognition and segmentation system is presented. We discuss the training and recognition phases of the phoneme recognition system as well as a detailed description of the integrated elements. The Hidden Markov Model (HMM) based phoneme models are trained using the Baum-Welch re-estimation procedure. Recognition and segmentation of the phonemes in the continuous speech is performed by a Segmental Viterbi Search on a Segmental Ergodic HMM for the phoneme states.

We describe in detail the three phases of the phoneme joint recognition and segmentation system. First, the extraction of the Mel-Frequency Cepstral Coefficients (MFCC) and the corresponding Delta and Delta Log Power coefficients is described. Second, we describe the operation of the Baum-Welch re-estimation procedure for the training of the phoneme HMM models, including the K-Means and the Expectation-Maximization (EM) clustering algorithms used for the initialization of the Baum-Welch algorithm. Additionally, we describe the structural framework of - and the recognition procedure for - the ergodic Segmental HMM for the phoneme segmentation and recognition. We include test and simulation results for each of the individual systems integrated into the phoneme recognition system and finally for the phoneme recognition/segmentation system as a whole.

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