AANN models for speaker recognition based on difference cepstrals

This paper presents a novel method for representing speaker characteristics present in the speech signal, by the way of deemphasizing the linguistic content of the signal. Cepstral coefficients that are widely employed as features for automatic speaker recognition task, contain considerable speech information in addition to the speaker information, and hence do not highlight the latter. The proposed method is based on using the difference between all-pole spectra due to higher order and lower order of linear prediction analysis. Distribution of the feature vectors in the multi-dimensional feature space is captured by employing autoassociative neural network models. A speaker recognition system is developed using the proposed method of feature extraction, whose performance is evaluated against that of the system based on cepstral coefficients. The complementary nature of evidence due to the proposed feature is also examined, so as to improve the overall system performance.