Browsing by Author "Lee, Sungmin"
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Item A Speech Processing Strategy Based on Sinusoidal Speech Model for Cochlear Implant Users(Institute of Electrical and Electronics Engineers Inc.) Lee, Sungmin; Akbarzadeh, Sara; Singh, Satnam; Tan, Chin-Tuan; 0000-0002-4676-4917 (Tan, C-T); 78509491 (Tan, C-T); Lee, Sungmin; Akbarzadeh, Sara; Singh, Satnam; Tan, Chin-TuanIn sinusoidal modeling (SM), speech signal, which is pseudo-periodic in structure, can be approximated by sinusoids and noise without losing significant speech information. A speech processing strategy based on this sinusoidal speech model will be relevant for encoding electric pulse streams in cochlear implant (CI) processing, where the number of channels available is limited. In this study, 5 normal hearing (NH) listeners and 2 CI users were asked to perform the task of speech recognition and perceived sound quality rating on speech sentences processed in 12 different test conditions. The sinusoidal analysis/synthesis algorithm was limited to 1, 3 or 6 sinusoids from the sentences low-pass filtered at either 1 kHz, 1.5 kHz, 3 kHz, or 6 kHz, re-synthesized as the test conditions. Each of 12 lists of AzBio sentences was randomly chosen and process with one of 12 test conditions, before they were presented to each participant at 65 dB SPL (Sound Pressure Level). Participant was instructed to repeat the sentence as they perceived, and the number of words correctly recognized was scored. They were also asked to rate the perceived sound quality of the sentences including original speech sentence, on the scale of 1 (distorted) to 10 (clean). Both speech recognition score and perceived sound quality rating across all participants increase when the number of sinusoids increases and low-pass filter broadens. Our current finding showed that three sinusoids may be sufficient to elicit the nearly maximum speech intelligibility and quality necessary for both NH and CI listeners. Sinusoidal speech model has the potential in facilitating the basis for a speech processing strategy in CI. ©2018 APSIPA.Item Predicting Speech Recognition Using the Speech Intelligibility Index and Other Variables for Cochlear Implant Users(American Speech-Language-Hearing Association, 2019-05-13) Lee, Sungmin; Mendel, L. L.; Bidelman, G. M.; Lee, SungminPurpose Although the speech intelligibility index (SII) has been widely applied in the field of audiology and other related areas, application of this metric to cochlear implants (CIs) has yet to be investigated. In this study, SIIs for CI users were calculated to investigate whether the SII could be an effective tool for predicting speech perception performance in a population with CI. Method Fifteen pre- and postlingually deafened adults with CI participated. Speech recognition scores were measured using the AzBio sentence lists. CI users also completed questionnaires and performed psychoacoustic (spectral and temporal resolution) and cognitive function (digit span) tests. Obtained SIIs were compared with predicted SIIs using a transfer function curve. Correlation and regression analyses were conducted on perceptual and demographic predictor variables to investigate the association between these factors and speech perception performance. Result Because of the considerably poor hearing and large individual variability in performance, the SII did not predict speech performance for this CI group using the traditional calculation. However, new SII models were developed incorporating predictive factors, which improved the accuracy of SII predictions in listeners with CI. Conclusion Conventional SII models are not appropriate for predicting speech perception scores for CI users. Demographic variables (aided audibility and duration of deafness) and perceptual-cognitive skills (gap detection and auditory digit span outcomes) are needed to improve the use of the SII for listeners with CI. Future studies are needed to improve our CI-corrected SII model by considering additional predictive factors. Supplemental Material https://doi.org/10.23641/asha.8057003.