Exploitation of spectral variance to improve robustness in speech recognition

With the aim of improving noise robustness of speech recognition an approach that exploits the variance information in spectral sub-bands is presented. The variance based features are used in combination with the normally used Mel-frequency cepstral coefficients (MFCC), and experimental results show that the combined features outperform MFCC alone, perceptual linear prediction features and entropy based features.