Speaker-independent French digits recognition using word-based vector quantization and hidden Markov models

2005 ◽  
Author(s):  
A. Tassy ◽  
L. Miclet
Keyword(s):  
Hidden Markov Models ◽  
Vector Quantization ◽  
Markov Models ◽  
Hidden Markov ◽  
Speaker Independent

RECOGNITION OF LEXICAL TONES FOR ISOLATED SYLLABLES AND DISYLLABLES IN MANDARIN SPEECH

1988 ◽  
Vol 02 (01) ◽  
pp. 49-69 ◽  
Author(s):  
WU-JI YANG ◽  
JYH-CHYANG LEE ◽  
YUEH-CHIN CHANG ◽  
HSIAO-CHUAN WANG
Keyword(s):  
Vector Quantization ◽  
Markov Models ◽  
Hidden Markov ◽  
Pitch Contour ◽  
Pitch Height ◽  
Accuracy Rate ◽  
Lexical Tones ◽  
Feature Vectors ◽  
Speaker Independent ◽  
Accuracy Rates

This study purposes a method for recognizing the lexical tones in Mandarin speech. The method is based on Vector Quantization (VQ) and Hidden Markov Models (HMM). The pitch periods are extracted to derive the feature vectors which represent pitch height and pitch contour slope. One HMM is trained by the feature vectors of monosyllables for each tone. Then the HMMs are used to recognize the tone of monosyllables and disyllables. For the monosyllables, the accuracy rate can be 93.75% for speaker-independent cases. For the disyllables, the accuracy rates are 93% for the first syllables and 90% for the second syllables. It shows that the tone of the second syllable may be affected by the preceding syllable. This degradation also reveals the fact of tone variation in Mandarin speech.

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