The use of artificial neural networks to estimate speech intelligibility from acoustic variables: a preliminary analysis.
Previous research has used regression analysis to attempt to predict the intelligibility of hearing-impaired speakers from acoustic speech parameters. Improvement of prediction may be achieved by the use of computerized artificial neural networks to process mathematically the acoustic input variables as part of the intelligibility process. A preliminary scheme for estimating speech intelligibility from acoustic parameters using a neural network is outlined and preliminary data illustrate its use.
Metz
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Department of Communication Research, NTID, Rochester Institute of Technology, NY 14623-0887.
Schiavetti
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Knight
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D000328:Adult; D001185:Artificial Intelligence; D001307:Auditory Perception; D005260:Female; D006311:Hearing Disorders; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D013061:Speech Acoustics; D013063:Speech Discrimination Tests; D013065:Speech Intelligibility