Perceptual harmonic cepstral coefficients for speech recognition in noisy environment

Perceptual harmonic cepstral coefficients (PHCC) are proposed as features to extract from speech for recognition in noisy environments. A weighting function, which depends on the prominence of the harmonic structure, is applied to the power spectrum to ensure accurate representation of the voiced speech spectral envelope. The harmonics´ weighted power spectrum undergoes mel-scaled band-pass filtering, and the log-energy of the filters´ output is discrete cosine transformed to produce cepstral coefficients. Lower spectral clipping is applied to the power spectrum, followed by within-filter root-power amplitude compression to reduce amplitude variation without compromise of the gain invariance properties. Experiments show significant recognition gains of PHCC over MFCC, with 23% and 36% error rate reduction for the Mandarin digit database in white and babble noise environments