AAAI Publications, Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence

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Identifying Hearing Deficiencies from Statistically Learned Speech Features for Personalized Tuning of Cochlear Implants
Bonny Banerjee, Lisa Lucks Mendel, Jayanta Kumar Dutta, Hasti Shabani, Shamima Najnin

Last modified: 2015-04-01


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.


cochlear implant tuning, hair cell, tuning curve, characteristic frequencies, feature learning, clustering, sparse coding

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