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Xiaosong Wang

Researcher at University of California, Los Angeles

Publications -  11
Citations -  1776

Xiaosong Wang is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Cochlear implant & Speech perception. The author has an hindex of 9, co-authored 11 publications receiving 1634 citations. Previous affiliations of Xiaosong Wang include House Ear Institute.

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Journal ArticleDOI

Speech recognition in noise as a function of the number of spectral channels: Comparison of acoustic hearing and cochlear implants

TL;DR: The results quantify the effect of number of spectral channels on speech recognition in noise and demonstrate that most CI subjects are not able to fully utilize the spectral information provided by the number of electrodes used in their implant.
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Effects of noise and spectral resolution on vowel and consonant recognition: acoustic and electric hearing.

TL;DR: The best cochlear implant user showed similar performance with the CIS strategy in quiet and in noise to that of normal-hearing listeners when listening to correspondingly spectrally degraded speech, suggesting that the noise susceptibility of co chlear implant users is at least partly due to the loss of spectral resolution.
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Consonant recordings for speech testing.

TL;DR: The results showed that the consonants were clearly recognized with only a few minor confusions, primarily between /v/ and /ð/.
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Moderate auditory training can improve speech performance of adult cochlear implant patients

TL;DR: It is suggested that moderate auditory training using a computer-based auditory rehabilitation tool can be an effective approach for improving the speech perception performance of cochlear implant patients.
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Development and validation of the Mandarin speech perception test

TL;DR: Mandarin speech perception (MSP) sentence test materials were developed and validated in normal-hearing subjects listening to acoustic simulations of CI processing and showed significant phonetic balancing within lists and validation with spectrally degraded speech.