Identifying hearing deficiencies from statistically learned speech features for personalized tuning of cochlear implants
Abstract
Cochlear implants (CIs) are an effective intervention for individuals with severe-to-profound sensorineural hearing loss. Currently, no tuning procedure exists that can fully exploit the technology. We propose online unsupervised algorithms to learn features from the speech of a severely-to- profoundly hearing-impaired patient round-the-clock and compare the features to those learned from the normal hearing population using a set of neurophysiological metrics. Experimental results are presented. The information from comparison can be exploited to modify the signal processing in a patient's CI to enhance his audibility of speech.
Publication Title
AAAI Workshop - Technical Report
Recommended Citation
Banerjee, B., Mendel, L., Dutta, J., Shabani, H., & Najnin, S. (2015). Identifying hearing deficiencies from statistically learned speech features for personalized tuning of cochlear implants. AAAI Workshop - Technical Report, WS-15-03, 4-5. Retrieved from https://digitalcommons.memphis.edu/facpubs/15392
