Auditory masking based acoustic front-end for robust speech recognition

This paper presents an acoustic front-end which uses the properties of auditory masking for extracting acoustic features from the speech signal. Using the properties of simultaneous masking found in the human auditory system, we compute a masking threshold as a function of frequency for a given speech frame from its power spectrum. All those portions of the power spectrum which are below the auditory threshold are not heard by the human auditory system due to masking effects and hence can be discarded. These portions are replaced by the corresponding portions in the masking threshold spectrum. This modified power spectrum is processed by the linear prediction analysis or homomorphic analysis procedure to derive cepstral features for each speech frame. We study the performance of this front-end for speech recognition under noisy environments. This front-end performs significantly better than the conventional linear prediction or homomorphic analysis based front-ends for noisy speech. In terms of signal-to-noise ratio, simultaneous masking offers an advantage of more than 5 dB over the LPCC front-end in isolated word recognition experiments and 3 dB in continuous speech recognition experiments.