On noise robust feature for speech recognition based on power function family
Hilman Ferdinandus Pardede · 2015
In this paper, a new feature robust against environmental noise is proposed for automatic speech recognition (ASR). This feature has similar extraction process with Power-Normalized Cepstral Coeffients (PNCC) except on two aspects. First, a generalization of the log function called the q-logarithmic function is applied to replace the power function and secondly, the mean normalization process is implemented before discrete cosine transform (DCT) instead of after it as in many traditional feature extraction algorithms. The proposed feature, called Q-Log Normalized Cepstral Coeffients (QLNCC), is shown more robust compared to two traditional features: MFCC and PLP. It is also better than PNCC without adding much complexity.