Abstract

Children speech recognition based on short-term spectral features is a challenging task. One of the reasons is that children speech has high fundamental frequency that is comparable to formant frequency values. Furthermore, as children grow, their vocal apparatus also undergoes changes. This presents difficulties in extracting standard short-term spectral-based features reliably for speech recognition. In recent years, novel acoustic modeling methods have emerged that learn both the feature and phone classifier in an end-to-end manner from the raw speech signal. Through an investigation on PF-STAR corpus we show that children speech recognition can be improved using end-to-end acoustic modeling methods. Finally, the simulations revealed that the proposed MFCC-CNN resulted in superior performance as compared to GMM model

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