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Published on: June 29, 2021
Relative Weights of Temporal Envelope Cues in Different Frequency Regions for Mandarin Vowel, Consonant, and Lexical
Zhong Zheng1,2, Keyi Li3, Gang Feng4
1Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Abstract:
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.
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