Pitch-Normalized Acoustic Features for Robust Children's Speech Recognition

Abstract

In this letter, the effectiveness of recently reported SMAC (Spectral Moment time–frequency distribution Augmented by low-order Cepstral) features has been evaluated for robust automatic speech recognition (ASR). The SMAC features consist of normalized first central spectral moments appended with low-order cepstral coefficients. These features have been designed for achieving robustness to both additive noise and the pitch variations. We have explored the SMAC features in severe pitch mismatch ASR task, i.e., decoding of children's speech on adults’ speech trained ASR system. In those tasks, the SMAC features are still observed to be sensitive to pitch variations. Toward addressing the same, a simple spectral smoothening approach employing adaptive-cepstral truncation is explored prior to the computation of spectral moments. With the proposed modification, the SMAC features are noted to achieve enhanced pitch robustness without affecting their noise immunity. Furthermore, the effectiveness of the proposed features is explored in three dominant acoustic modeling paradigms and varying data conditions. In all the cases, the proposed features are observed to significantly outperform the existing ones.