Padmalochini Umakanthan and Kaliappan Gopalan
Psychoacoustics, speech recognition, dynamic time warping, cepstral features
This paper proposes a set of features based on the psychoacoustic masking phenomenon of human auditory system for speech recognition. Features are determined using the difference between spectral energy of speech frames and their global masking thresholds in each of 17 bands of an utterance. Performance of the proposed features in a keyword spotting experiment employing dynamic time warping for feature matching showed the viability of the perceptually significant feature set. For multisyllabic words, features from both the proposed set and mel frequency cepstral coefficients (MFCCs) performed equally while for monosyllabic words the proposed set outperformed MFCCs.
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