Modified Feature Extraction Methods in Robust Speech Recognition

Abstract

The speech recognisers use a parametric form of the signal to get the most important features in speech for the recognition task. Mel-frequency cepstral coefficients (MFCC) and Perceptual linear prediction coefficients (PLP) belong to the most commonly used methods. There is no rule to decide which one is better to use and it depends mainly on the particular conditions. The tests on taking advantage of different parts of each parametrization process to get the best results in given conditions are presented in this paper. Robust Hidden Markov model-based (HMM) Czech digit recogniser in slightly noisy environment is used for this purpose. The experiments show, that using Bark-frequency scaling, equal loudness pre-emphasis and intensity-loudness power law in the original MFCC method can bring improvement in white noise robustness for particular conditions. The results also uncovered that the LP-based methods tend to generate insertion errors in given environment.