Cantonese lexical tone recognition from frequency-specific temporal envelope and periodicity components in the same versus different noise band carriers

2009 ◽  
pp. n/a-n/a
Author(s):  
Kevin C P Yuen ◽  
Michael C F Tong ◽  
Charles A. Van Hasselt ◽  
Meng Yuan ◽  
Tan Lee ◽  
...  
Keyword(s):  
Noise Band ◽  
Lexical Tone ◽  
Temporal Envelope ◽  
Tone Recognition ◽  
Lexical Tone Recognition

Cantonese lexical tone recognition from frequency-specific temporal envelope and periodicity components in the same versus different noise band carriers

2009 ◽  
Vol 10 (sup1) ◽  
pp. 148-158
Author(s):  
Kevin CP Yuen ◽  
Michael CF Tong ◽  
Van Charles A Hasselt ◽  
Meng Yuan ◽  
Tan Lee ◽  
...  
Keyword(s):  
Noise Band ◽  
Lexical Tone ◽  
Temporal Envelope ◽  
Tone Recognition ◽  
Lexical Tone Recognition

scholarly journals Relative Weights of Temporal Envelope Cues in Different Frequency Regions for Mandarin Vowel, Consonant, and Lexical Tone Recognition

2021 ◽  
Vol 15 ◽  
Author(s):  
Zhong Zheng ◽  
Keyi Li ◽  
Gang Feng ◽  
Yang Guo ◽  
Yinan Li ◽  
...  
Keyword(s):  
Speech Coding ◽  
Frequency Region ◽  
Lexical Tone ◽  
Temporal Envelope ◽  
Percent Correct ◽  
Coding Schemes ◽  
Consonant Recognition ◽  
Tone Recognition ◽  
Vowel Recognition ◽  
Lexical Tone Recognition

Objectives: Mandarin-speaking users of cochlear implants (CI) perform poorer than their English counterpart. This may be because present CI speech coding schemes are largely based on English. This study aims to evaluate the relative contributions of temporal envelope (E) cues to Mandarin phoneme (including vowel, and consonant) and lexical tone recognition to provide information for speech coding schemes specific to Mandarin.Design: Eleven normal hearing subjects were studied using acoustic temporal E cues that were extracted from 30 continuous frequency bands between 80 and 7,562 Hz using the Hilbert transform and divided into five frequency regions. Percent-correct recognition scores were obtained with acoustic E cues presented in three, four, and five frequency regions and their relative weights calculated using the least-square approach.Results: For stimuli with three, four, and five frequency regions, percent-correct scores for vowel recognition using E cues were 50.43–84.82%, 76.27–95.24%, and 96.58%, respectively; for consonant recognition 35.49–63.77%, 67.75–78.87%, and 87.87%; for lexical tone recognition 60.80–97.15%, 73.16–96.87%, and 96.73%. For frequency region 1 to frequency region 5, the mean weights in vowel recognition were 0.17, 0.31, 0.22, 0.18, and 0.12, respectively; in consonant recognition 0.10, 0.16, 0.18, 0.23, and 0.33; in lexical tone recognition 0.38, 0.18, 0.14, 0.16, and 0.14.Conclusion: Regions that contributed most for vowel recognition was Region 2 (502–1,022 Hz) that contains first formant (F1) information; Region 5 (3,856–7,562 Hz) contributed most to consonant recognition; Region 1 (80–502 Hz) that contains fundamental frequency (F0) information contributed most to lexical tone recognition.

Mandarin lexical tone recognition in bimodal cochlear implant users

2020 ◽  
Vol 59 (7) ◽  
pp. 548-555
Author(s):  
Qian Zhou ◽  
Jintao Bi ◽  
Haoheng Song ◽  
Xin Gu ◽  
Bo Liu
Keyword(s):  
Cochlear Implant ◽  
Lexical Tone ◽  
Tone Recognition ◽  
Lexical Tone Recognition

Tone nucleus modeling for Chinese lexical tone recognition

2004 ◽  
Vol 42 (3-4) ◽  
pp. 447-466 ◽  
Author(s):  
Jinsong Zhang ◽  
Keikichi Hirose
Keyword(s):  
Lexical Tone ◽  
Tone Recognition ◽  
Lexical Tone Recognition

Hidden Markov model for Mandarin lexical tone recognition

1988 ◽  
Vol 36 (7) ◽  
pp. 988-992 ◽  
Author(s):  
W.-J. Yang ◽  
J.-C. Lee ◽  
Y.-C. Chang ◽  
H.-C. Wang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Lexical Tone ◽  
Tone Recognition ◽  
Lexical Tone Recognition
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