Auditory image model features for automatic speech recognition
نویسندگان
چکیده
Conventional speech recognition engines extract Mel Frequency Cepstral Coefficients (MFCC) features from incoming speech. This paper presents a novel approach for feature extraction in which speech is processed according to the Auditory Image Model, a model of human psychoacoustics. We fist describe the proposed frontend, then we present recognition results obtained with the TIMIT database. Comparing with previously published results on the same task, the new approach achieves a 10% improvement in recognition accuracy.
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تاریخ انتشار 2005