Level-Building on AdaBoost HMM Classifiers and the Application to Visual Speech Processing(Speech and Hearing)

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概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 2004-11-01

著者

• Foo S‐w

  School Of Electrical And Electronic Engineering Nanyang Technological University

• Lian Yong

  School Of Electrical And Electronic Engineering Nanyang Technological University

• Foo Say-wei

  School Of Eee Nanyang Technological University

• DONG Liang

  School of Electrical and Electronic Engineering, Nanyang Technological University

• Dong Liang

  School Of Electrical And Electronic Engineering Nanyang Technological University

関連論文

• Computer Aided Detection of Breast Masses from Digitized Mammograms(Biological Engineering)
• Level-Building on AdaBoost HMM Classifiers and the Application to Visual Speech Processing(Speech and Hearing)
• Boundary Detection in Echocardiographic Images Using Markovian Level Set Method(Image Recognition, Computer Vision)
• Speaker Recognition Using Adaptively Boosted Classifiers (Special Issue on Speech Information Processing)

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