Robust Feature Based Automatic Text-Independent Gender Identification System Using Ergodic Hidden Markov Models(HMMs) (International Journal of Engineering and Computer Science, 2014)
In this paper, robust feature for Automatic text-independent Gender Identification System has been explored. Through different experimental studies, it is demonstrated that the timing varying speech related information can be effectively captured using Hidden Markov  Models (HMMs). The study on the effect of feature vector size for good Gender Identification   demonstrates that, feature vector size in the range of 18-22 can capture Gender related information effectively for a speech signal sampled at 16 kHz, it is established that the proposed Gender Identification system requires significantly less amount of data during both during training as well as in testing. The Gender Identification study using robust features for different states  and different mixtures components, training and test duration has been exploited. I demonstrate the Gender Identification studies on TIMIT database.