The paper presents a parallel neural networks approach to speaker-independent all-Chinese-syllable speech recognition. The author uses neural networks to recognize 59 speech units, 22 initial consonants, 36 syllable finals and a background noise, for pattern division. Each binary classifier recognizes a speech unit to discriminate P/sub i/ from /spl sim/P/sub i/ (NoT P/sub i/). The utterances from 137 male speakers are used to train and the utterances from other 11 male speakers are used to recognize. It achieved a recognition correct rate of 66.14% for Chinese syllables, 73.06% for initial consonants and 84.6% for syllable finals. They are rather good without grammer.<>
Parallel neural networks for speaker-independent all-Chinese-syllable speech recognition
01.01.1994
306405 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Parallel Neural Networks for Speaker-Independent All-Chinese-Syllable Speech Recognition
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