  Short time spectral features such as mel frequency cepstral
coefficients(MFCCs) have been previously deployed in state of the art speaker
recognition systems, however lesser heed has been paid to short term spectral
features that can be learned by generative learning models from speech signals.
Higher dimensional encoders such as deep belief networks (DBNs) could improve
performance in speaker recognition tasks by better modelling the statistical
structure of sound waves. In this paper, we use short term spectral features
learnt from the DBN augmented with MFCC features to perform the task of speaker
recognition. Using our features, we achieved a recognition accuracy of 0.95 as
compared to 0.90 when using standalone MFCC features on the ELSDSR dataset.
