Autoregressive modeling of speech trajectory transformed to the reconstructed phase space for ASR purposes

Investigating new effective feature extraction methods applied to the speech signal is an important approach to improve the performance of automatic speech recognition (ASR) systems. Owing to the fact that the reconstructed phase space (RPS) is a proper field for true detection of signal dynamics, in this paper we propose a new method for feature extraction from the trajectory of the speech signal in the RPS. This method is based upon modeling the speech trajectory using the multivariate autoregressive (MVAR) method. Moreover, in the following, we benefit from linear discriminant analysis (LDA) for dimension reduction. The LDA technique is utilized to simultaneously decorrelate and reduce the dimension of the final feature set. Experimental results show that the MVAR of order 6 is appropriate for modeling the trajectory of speech signals in the RPS. In this study recognition experiments are conducted with an HMM-based continuous speech recognition system and a naive Bayes isolated phoneme classifier on the Persian FARSDAT and American English TIMIT corpora to compare the proposed features to some older RPS-based and traditional spectral-based MFCC features.