Assessment of pitch-adaptive front-end signal processing for children's speech recognition

On account of large acoustic mismatch, automatic speech recognition (ASR) systems trained using adults' speech data yield poor recognition performance when evaluated on children's speech data. Despite the use of common speaker normalization techniques like feature-space maximum likelihood regression (fMLLR) and vocal tract length normalization (VTLN), a significant gap remains between the recognition rates for matched and mismatched testing. Our earlier works have already highlighted the sensitivity of salient front-end features including the popular Mel-frequency cepstral coefficient (MFCC) to gross pitch variation across adult and child speakers. Motivated by that, in this work, we explore pitch-adaptive front-end signal processing in deriving the MFCC features to reduce the sensitivity to pitch variation. For this purpose, first an existing vocoder approach known as STRAIGHT spectral analysis is employed for obtaining the smoothed spectrum devoid of pitch harmonics. Secondly, a much simpler spectrum smoothing approach exploiting pitch adaptive-liferting is also presented. The proposed approach is noted to be less sensitive to errors in the pitch estimation than the STRAIGHT-based approach. Both these approaches result in significant improvements for children's mismatch ASR. The effectiveness of the proposed adaptive-liftering-based approach is also demonstrated in the context of acoustic modeling paradigms based on the subspace Gaussian mixture model (SGMM) and the deep neural network (DNN). Further, it has been shown that the effectiveness of existing speaker normalization techniques remain intact even with the use of proposed pitch-adaptive MFCCs, thus leading to additional gains.