Neural Predictive Hidden Markov Model for Speech Recognition

Eiichi TSUBOKA; Yoshihiro TAKADA

Neural Predictive Hidden Markov Model for Speech Recognition

Eiichi TSUBOKA, Yoshihiro TAKADA

Full Text Views

0

Share
Cite this

Summary :

This paper describes new modeling methods combining neural network and hidden Markov model applicable to modeling a time series such as speech signal. The idea assumes that the sequence is nonstationary and is a nonlinear autoregressive process whose parameters are controlled by a hidden Markov chain. One is the model where a non-linear predictor composed of a multi-layered neural network is defined at each state, another is the model where a multi-layered neural network is defined so that the path from the input layer to the output layer is divided into path-groups each of which corresponds to the state of the Markov chain. The latter is an extended model of the former. The parameter estimation methods for these models are shown, and other previously proposed models--one called Neural Prediction Model and another called Linear Predictive HMM--are shown to be special cases of the NPHMM proposed here. The experimental result affirms the justification of these proposed models.

Publication: IEICE TRANSACTIONS on Information Vol.E78-D No.6 pp.676-684

Publication Date: 1995/06/25

Publicized

Online ISSN

DOI

Type of Manuscript: Special Section PAPER (Special Issue on Spoken Language Processing)

Category

Cite this

Copy

Eiichi TSUBOKA, Yoshihiro TAKADA, "Neural Predictive Hidden Markov Model for Speech Recognition" in IEICE TRANSACTIONS on Information, vol. E78-D, no. 6, pp. 676-684, June 1995, doi: .
Abstract: This paper describes new modeling methods combining neural network and hidden Markov model applicable to modeling a time series such as speech signal. The idea assumes that the sequence is nonstationary and is a nonlinear autoregressive process whose parameters are controlled by a hidden Markov chain. One is the model where a non-linear predictor composed of a multi-layered neural network is defined at each state, another is the model where a multi-layered neural network is defined so that the path from the input layer to the output layer is divided into path-groups each of which corresponds to the state of the Markov chain. The latter is an extended model of the former. The parameter estimation methods for these models are shown, and other previously proposed models--one called Neural Prediction Model and another called Linear Predictive HMM--are shown to be special cases of the NPHMM proposed here. The experimental result affirms the justification of these proposed models.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e78-d_6_676/_p

Copy

@ARTICLE{e78-d_6_676,
author={Eiichi TSUBOKA, Yoshihiro TAKADA, },
journal={IEICE TRANSACTIONS on Information},
title={Neural Predictive Hidden Markov Model for Speech Recognition},
year={1995},
volume={E78-D},
number={6},
pages={676-684},
abstract={This paper describes new modeling methods combining neural network and hidden Markov model applicable to modeling a time series such as speech signal. The idea assumes that the sequence is nonstationary and is a nonlinear autoregressive process whose parameters are controlled by a hidden Markov chain. One is the model where a non-linear predictor composed of a multi-layered neural network is defined at each state, another is the model where a multi-layered neural network is defined so that the path from the input layer to the output layer is divided into path-groups each of which corresponds to the state of the Markov chain. The latter is an extended model of the former. The parameter estimation methods for these models are shown, and other previously proposed models--one called Neural Prediction Model and another called Linear Predictive HMM--are shown to be special cases of the NPHMM proposed here. The experimental result affirms the justification of these proposed models.},
keywords={},
doi={},
ISSN={},
month={June},}

Copy

TY - JOUR
TI - Neural Predictive Hidden Markov Model for Speech Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 676
EP - 684
AU - Eiichi TSUBOKA
AU - Yoshihiro TAKADA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E78-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 1995
AB - This paper describes new modeling methods combining neural network and hidden Markov model applicable to modeling a time series such as speech signal. The idea assumes that the sequence is nonstationary and is a nonlinear autoregressive process whose parameters are controlled by a hidden Markov chain. One is the model where a non-linear predictor composed of a multi-layered neural network is defined at each state, another is the model where a multi-layered neural network is defined so that the path from the input layer to the output layer is divided into path-groups each of which corresponds to the state of the Markov chain. The latter is an extended model of the former. The parameter estimation methods for these models are shown, and other previously proposed models--one called Neural Prediction Model and another called Linear Predictive HMM--are shown to be special cases of the NPHMM proposed here. The experimental result affirms the justification of these proposed models.
ER -