Complementarity of MFCC, PLP and Gabor features in the presence of speech-intrinsic variabilities

In this study, the effect of speech-intrinsic variabilities such as speaking rate, effort and speaking style on automatic speech recognition (ASR) is investigated. We analyze the influence of such variabilities as well as extrinsic factors (i.e., additive noise) on the most common features in ASR (mel-frequency cepstral coefficients and perceptual linear prediction features) and spectro-temporal Gabor features. MFCCs performed best for clean speech, whereas Gabors were found to be the most robust feature in extrinsic variabilities. Intrinsic variations were found to have a strong impact on error rates. While performance with MFCCs and PLPs was degraded in much the same way, Gabor features exhibit a different sensivity towards these variabilities and are, e.g., well-suited to recognize speech with varying pitch. The results suggest that spectro-temporal and classic features carry complementary information, which could be exploited in feature-stream experiments.