The Use of Discrete Hidden Markov Model with a Very Large Codebook for Automatic Speech Recognition

PhD Thesis Proposal Defence


Title: "The Use of Discrete Hidden Markov Model with a Very Large Codebook for
Automatic Speech Recognition"

by

Mr. Guoli Ye


ABSTRACT:

With the advance of semiconductor technology and the popularity of 
distributed automatic speech recognition (ASR) paradigm (e.g., Siri in 
iPhone4s), we would like to revisit the discrete hidden Markov model 
(DHMM) as the acoustic model in ASR. Compared with continuous density 
hidden Markov model (CDHMM), the dominant acoustic model used in modern 
ASR systems, DHMM has inherently attractive properties: it uses 
non-parametric state output distributions and takes only O(1) time to get 
the probability value from it; Furthermore, the discrete features used in 
DHMM, compared with cepstral coefficients in CDHMM, could be encoded in 
fewer bits, lowering the bandwidth requirement in distributed speech 
recognition architecture. Unfortunately, the recognition performance of 
conventional DHMMis significantly worse than that of CDHMM due to the 
large quantization error and the use of multiple independent streams. In 
this proposal, we propose to reduce the quantization error of DHMM by 
using a very large codebook with tens of thousands of codewords (in 
conventional DHMM, the number of codewords in a codebook usually ranges 
from 256 to 1024). An extensive literature review is given in the proposal 
to show that very large codebook in DHMMis novel and necessary. The 
challenges to use large codebook are discussed, with a novel framework 
called subspace high-density discrete HMM (SHDDHMM) to solve the problems. 
A large vocabulary continuous speech recognition task is used to evaluate 
the proposed framework, showing the feasibility and benefits of DHMM with 
very large codebook. Future works for completion of the thesis are also 
given in the proposal.


Date:                   Thursday, 7 June 2012

Time:                   2:00pm - 4:00pm

Venue:                  Room 3315
                         lifts 17/18

Committee Members:      Dr. Brian Mak (Supervisor)
                         Prof. James Kwok (Chairperson)
 			Dr. Raymond Wong
 			Prof. Dit-Yan Yeung


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