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Use of a self-learning neuro-fuzzy system for syllabic labeling of continuous speech
Ching Tang Hsieh,
Mu Chun Su
, Shih Chieh Chienn
資訊工程學系
研究成果
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引文 斯高帕斯(Scopus)
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Keyphrases
Composite Neural Network
100%
Computation Complexity
25%
Consonants
25%
Continuous Speech
100%
Correct Recognition Rate
25%
Crispy
25%
Fuzzy Classification Rules
25%
Fuzzy Neural Network
100%
Fuzzy Rules
25%
Hybrid System
25%
Large Memory
25%
Large Vocabulary Continuous Speech Recognition (LVCSR)
25%
Mandarin Speech
25%
Network Paradigm
25%
Neural Network
25%
Novel Class
25%
Reading Rate
25%
Rule-based Approach
25%
Segmentation Feature
25%
Segmentation Model
25%
Speech Data
50%
Speech Recognition System
50%
Speech Segmentation
75%
Syllabic Units
50%
Transition State
25%
Two-phase Problem
25%
Unit-based
25%
Vowels
25%
Engineering
Classification Rule
50%
Computation Complexity
50%
Continuous Speech Recognition
50%
Fuzzy Rules
50%
Fuzzy System
100%
Illustrates
50%
Recognition Rate
50%
Speech Data
100%
Computer Science
Classification Rule
20%
Computation Complexity
20%
fuzzy classification
20%
Network Paradigm
20%
Neural Network
100%
neuro-fuzzy
100%
Recognition Rate
20%
Segmentation Model
20%
Speech Recognition System
40%