School of Communication Sciences and Disorders Speech Perception Assessment Laboratory

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Audiology/Hearing Science
Speech Pathology/Speech Science
Language/Language Science

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Current Research

Algorithms for Unsupervised and Online Learning of Hierarchy of Features for Tuning Cochlear Implants for the Hearing Impaired

In this project we will develop and use machine learning algorithms to tune hearing instruments, particularly cochlear implants, based on each individual’s hearing characteristics and speech production errors. The speech production capabilities of individuals with severe to profound sensorineural hearing loss will be analyzed with the assumption that deficiencies in their speech production output are a reflection of their poor speech perception capabilities. The speech production data will be analyzed, and algorithms will be developed to determine modifications that can be made to hearing instruments to improve speech perception. Ongoing samples of normal hearing and hearing-impaired speech will be analyzed to document the speech characteristics and deficiencies from these two populations. The missing and distorted features from the hearing-impaired speech will be identified, and algorithms will be developed that will ultimately be used to improve the signal processing strategies used in hearing instruments to enhance the audibility of speech features for hearing-impaired individuals.

This award is through the Smart Health and Wellbeing (SHB) Program of NSF that seeks to address fundamental technical and scientific issues that support transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, person-centered and focused on wellbeing rather than disease.

The NSF Project webpage can be found here.

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Our MA and AUD programs are accredited by the Council on Academic Accreditation (CAA) of the American Speech-Language-Hearing Association.
Last Updated: 12/21/12