IEICE TRANSACTIONS on Information
Level-Building on AdaBoost HMM Classifiers and the Application to Visual Speech Processing
Summary :
The Hidden Markov Model (HMM) is a popular statistical framework for modeling and analyzing stochastic signals. In this paper, a novel strategy is proposed that makes use of level-building algorithm with a chain of AdaBoost HMM classifiers to model long stochastic processes. AdaBoost HMM classifier belongs to the class of multiple-HMM classifier. It is specially trained to identify samples with erratic distributions. By connecting the AdaBoost HMM classifiers, processes of arbitrary length can be modeled. A probability trellis is created to store the accumulated probabilities, starting frames and indices of each reference model. By backtracking the trellis, a sequence of best-matched AdaBoost HMM classifiers can be decoded. The proposed method is applied to visual speech processing. A selected number of words and phrases are decomposed into sequences of visual speech units using both the proposed strategy and the conventional level-building on HMM method. Experimental results show that the proposed strategy is able to more accurately decompose words/phrases in visual speech than the conventional approach.
- Publication
- IEICE TRANSACTIONS on Information Vol.E87-D No.11 pp.2460-2471
- Publication Date
- 2004/11/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Speech and Hearing
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Liang DONG, Say-Wei FOO, Yong LIAN, "Level-Building on AdaBoost HMM Classifiers and the Application to Visual Speech Processing" in IEICE TRANSACTIONS on Information,
vol. E87-D, no. 11, pp. 2460-2471, November 2004, doi: .
Abstract: The Hidden Markov Model (HMM) is a popular statistical framework for modeling and analyzing stochastic signals. In this paper, a novel strategy is proposed that makes use of level-building algorithm with a chain of AdaBoost HMM classifiers to model long stochastic processes. AdaBoost HMM classifier belongs to the class of multiple-HMM classifier. It is specially trained to identify samples with erratic distributions. By connecting the AdaBoost HMM classifiers, processes of arbitrary length can be modeled. A probability trellis is created to store the accumulated probabilities, starting frames and indices of each reference model. By backtracking the trellis, a sequence of best-matched AdaBoost HMM classifiers can be decoded. The proposed method is applied to visual speech processing. A selected number of words and phrases are decomposed into sequences of visual speech units using both the proposed strategy and the conventional level-building on HMM method. Experimental results show that the proposed strategy is able to more accurately decompose words/phrases in visual speech than the conventional approach.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_11_2460/_p
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@ARTICLE{e87-d_11_2460,
author={Liang DONG, Say-Wei FOO, Yong LIAN, },
journal={IEICE TRANSACTIONS on Information},
title={Level-Building on AdaBoost HMM Classifiers and the Application to Visual Speech Processing},
year={2004},
volume={E87-D},
number={11},
pages={2460-2471},
abstract={The Hidden Markov Model (HMM) is a popular statistical framework for modeling and analyzing stochastic signals. In this paper, a novel strategy is proposed that makes use of level-building algorithm with a chain of AdaBoost HMM classifiers to model long stochastic processes. AdaBoost HMM classifier belongs to the class of multiple-HMM classifier. It is specially trained to identify samples with erratic distributions. By connecting the AdaBoost HMM classifiers, processes of arbitrary length can be modeled. A probability trellis is created to store the accumulated probabilities, starting frames and indices of each reference model. By backtracking the trellis, a sequence of best-matched AdaBoost HMM classifiers can be decoded. The proposed method is applied to visual speech processing. A selected number of words and phrases are decomposed into sequences of visual speech units using both the proposed strategy and the conventional level-building on HMM method. Experimental results show that the proposed strategy is able to more accurately decompose words/phrases in visual speech than the conventional approach.},
keywords={},
doi={},
ISSN={},
month={November},}
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TY - JOUR
TI - Level-Building on AdaBoost HMM Classifiers and the Application to Visual Speech Processing
T2 - IEICE TRANSACTIONS on Information
SP - 2460
EP - 2471
AU - Liang DONG
AU - Say-Wei FOO
AU - Yong LIAN
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2004
AB - The Hidden Markov Model (HMM) is a popular statistical framework for modeling and analyzing stochastic signals. In this paper, a novel strategy is proposed that makes use of level-building algorithm with a chain of AdaBoost HMM classifiers to model long stochastic processes. AdaBoost HMM classifier belongs to the class of multiple-HMM classifier. It is specially trained to identify samples with erratic distributions. By connecting the AdaBoost HMM classifiers, processes of arbitrary length can be modeled. A probability trellis is created to store the accumulated probabilities, starting frames and indices of each reference model. By backtracking the trellis, a sequence of best-matched AdaBoost HMM classifiers can be decoded. The proposed method is applied to visual speech processing. A selected number of words and phrases are decomposed into sequences of visual speech units using both the proposed strategy and the conventional level-building on HMM method. Experimental results show that the proposed strategy is able to more accurately decompose words/phrases in visual speech than the conventional approach.
ER -
English
