title: Model adaptation method for recognition of speech with missing frames.
abstract: In distributed speech recognition (DSR), data packets may be lost over error prone channels. A commonly used approach to rectify this is to reconstruct a full frame rate data sequence for recognition using linear interpolation. In this study, an error-concealment decoding method that dynamically adapts the transition probabilities of hidden Markov models to match the frame loss observation sequence is proposed. Experimental results show that a DSR system using the proposed method can achieve the same level of accuracy as a data reconstruction method, is more robust against heavy frame loss, and significantly reduces the computation time.
authors.affiliation: Department of Electrical Engineering, Dayeh University, 168 University Road, Dacun, Changhua, Taiwan lmlee@mail.dyu.edu.tw.
authors.forename: Lee-Min
authors.initials: LM
authors.lastname: Lee
authors.affiliation: Department of Electrical Engineering, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao East Road, Taipei, Taiwan frjean@ntut.edu.tw.
authors.forename: Fu-Rong
authors.initials: FR
authors.lastname: Jean
mesh_terms: D000162:Acoustics; D000465:Algorithms; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013061:Speech Acoustics; D013068:Speech Production Measurement