Mandarin Chinese tone identification in cochlear implants: Predictions from acoustic models
It has been established that current cochlear implants do not supply adequate spectral information for perception of tonal languages. Comprehension of a tonal language, such as Mandarin Chinese, requires recognition of lexical tones. New strategies of cochlear stimulation such as variable stimulatio...
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          | Published in | Hearing research Vol. 244; no. 1; pp. 66 - 76 | 
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| Main Authors | , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Amsterdam
          Elsevier B.V
    
        01.10.2008
     Elsevier  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 0378-5955 1878-5891 1878-5891  | 
| DOI | 10.1016/j.heares.2008.07.008 | 
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| Summary: | It has been established that current cochlear implants do not supply adequate spectral information for perception of tonal languages. Comprehension of a tonal language, such as Mandarin Chinese, requires recognition of lexical tones. New strategies of cochlear stimulation such as variable stimulation rate and current steering may provide the means of delivering more spectral information and thus may provide the auditory fine-structure required for tone recognition. Several cochlear implant signal processing strategies are examined in this study, the continuous interleaved sampling (CIS) algorithm, the frequency amplitude modulation encoding (FAME) algorithm, and the multiple carrier frequency algorithm (MCFA). These strategies provide different types and amounts of spectral information. Pattern recognition techniques can be applied to data from Mandarin Chinese tone recognition tasks using acoustic models as a means of testing the abilities of these algorithms to transmit the changes in fundamental frequency indicative of the four lexical tones. The ability of processed Mandarin Chinese tones to be correctly classified may predict trends in the effectiveness of different signal processing algorithms in cochlear implants. The proposed techniques can predict trends in performance of the signal processing techniques in quiet conditions but fail to do so in noise. | 
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1  | 
| ISSN: | 0378-5955 1878-5891 1878-5891  | 
| DOI: | 10.1016/j.heares.2008.07.008 |