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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46193

Title: New speech recognition approaches based upon finite state vector quantization with structural constraints
Authors: Pei-yih Ting
Chiu-yu Tseng
Lin-shan Lee
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Speech recognition;Vector quantization;Automata;Hidden Markov models;Algorithm design and analysis;Delay;Testing;Encoding;Laboratories;History
Date: 1988-04
Issue Date: 2018-05-07T07:33:34Z
Publisher: Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on
Abstract: Abstract:
The label-transition finite-state vector-quantization (FSVQ) algorithm is extensively explored to exhibit the power of finite-state machines for speech recognition. It is found that the FSVQ algorithm combined with special structural constraints can discriminate a finite set of candidates very successfully. All the consonant initials of isolated Mandarin monosyllables from designated speakers are used as the example vocabulary in the simulation. In addition to utilizing the first order memory provided by FSVQ on speech recognition, an experiment is conducted that expands the FSVQ to use the second-order memory and the dynamic relationship among the components of this three-vector group are used for recognition. The simulation results show that a slightly higher recognition rate (94.4%) is obtained with a consistent prediction interval.
Relation: pp. 187-190
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46193
Appears in Collections:[資訊工程學系] 演講及研討會

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