Objective measures based on neural networks for hearing loss compensation techniques

Author

Tungthangthum, Apichat ; Rutledge, Janet C.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA

fYear

1993

fDate

17-20 Oct 1993

Firstpage

93

Lastpage

96

Abstract

An objective measures system has been developed to predict the results of subject-based tests for sensorineural hearing loss compensation techniques. Parameters related to the loudness level of the compensated speech signal are extracted from its frequency spectrum. These parameters are then used to train a neural network based phoneme classifier. Good prediction results have been achieved for two hearing impaired subjects

Keywords

acoustic signal processing; hearing; hearing aids; learning (artificial intelligence); medical signal processing; neural nets; spectral analysis; speech intelligibility; speech processing; compensated speech signal; frequency spectrum; hearing impaired subject; hearing loss compensation; loudness level; neural networks; objective measures system; phoneme classifier; sensorineural hearing loss compensation; subject-based tests; training; Auditory system; Deafness; Dynamic range; Frequency; Loss measurement; Neural networks; Pain; Pattern recognition; Speech processing; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Applications of Signal Processing to Audio and Acoustics, 1993. Final Program and Paper Summaries., 1993 IEEE Workshop on

Conference_Location

New Paltz, NY

Print_ISBN

0-7803-2078-6

Type

conf

DOI

10.1109/ASPAA.1993.379988

Filename

379988