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|Title: ||Multiple-Regression Hidden Markov Model|
|Authors: ||Fujinaga, Katsuhisa|
|Issue Date: ||2001|
|Citation: ||In Proc. ICASSP 2001, May 2001.|
|Abstract: ||This paper proposes a new class of hidden Markov
model (HMM) called multiple-regression HMM (MRHMM)
that utilizes auxiliary features such as fundamental
frequency (F0) and speaking styles that affect spectral parameters
to better model the acoustic features of phonemes.
Though such auxiliary features are considered to be the factors
that degrade the performance of speech recognizers, the
proposed MR-HMM adapts its model parameters, i.e. mean
vectors of output probability distributions, depending on
these auxiliary information to improve the recognition accuracy.
Formulation for parameter reestimation of MRHMM
based on the EM algorithm is given in the paper.
Experiments of speaker-dependent isolated word recognition
demonstrated that MR-HMMs using F0 based auxiliary
features reduced the error rates by more than 20% compared
with the conventional HMMs.|
|Keywords: ||hidden Markov model|
|Appears in Collections:||CSTR publications|
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