A comparison between speech signal representation using linear prediction and Gabor transform

Feature extraction from speech representation is one of the processes in speech recognition. Parametric modeling is a dominant approach to model speech signals. Within a localized interval, speech representation is equivalent to a noise driven output from an all-pole system that can be estimated using linear prediction. Besides the characteristics of speech, temporal variability of speech signal model is also due to the computation of linear prediction coefficients. Thus, an alternative representation is proposed based on the Gabor coefficients. In this paper, a comparison is made with the linear prediction coefficients to show the consistency of the parameters that are generated for implementation in the speech recognition system.