Abstract
A front end for automatic speech recognizers is proposed and evaluated which is based on a quantitative model of the "effective" peripheral auditory processing. The model simulates both spectral and temporal properties of sound processing in the auditory system which were found in psychoacoustical and physiological experiments. The robustness of the auditory-based representation of speech was evaluated in speaker-independent, isolated word recognition experiments in different types of additive noise. The results show a higher robustness of the auditory front end in noise, compared to common mel-scale cepstral feature extraction. In a second set of experiments, different processing stages of the auditory front end were modified to study their contribution to robust speech signal representation in detail. The adaptive compression stage which enhances temporal changes of the input signal appeared to be the most important processing stage towards robust speech representation in noise. Low-pass filtering of the fast fluctuating envelope in each frequency band further reduces the influence of noise in the auditory-based representation of speech.
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