Pitch adaptive MFCC features for improving children’s mismatched ASR

A pitch normalization algorithm is proposed for addressing the pitch mismatch between adults’ and children’s speech for children’s automatic speech recognition (ASR). Motivated by the appearance of pitch-dependent distortions in the smoothed mel spectral envelope for high-pitched children’s speech, the algorithm modifies the mel filterbank during MFCC feature extraction to improve ASR performance. Relative improvements of 16 % and 9 % are obtained over the corresponding baseline in children’s mismatched ASR performance on a connected-digit recognition task and a continuous speech recognition task. The improvements obtained in ASR performance with the proposed pitch normalization algorithm are also found to be additive to that obtained with existing speaker normalization techniques, VTLN and CMLLR.

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