Performance evaluation of Statistical Approaches for Automatic Text-Independent Speaker Recognition using Robust Features
This paper introduces the performance evaluation of statististical approaches for Automatic-text-independent Speaker Recognition system. Automatic-text-independent Speaker Recognition system is to quickly and accurately identify the person from his/her voice. The study on the effect of feature vector size for good speaker recognition demonstrates that the feature vector size in the range of 18-22 can capture speaker related information effectively for a speech signal sampled at 16 kHz. it is demonstrated that the timing varying speaker related information can be effectively captured using hidden Markov models (HMMs) than GMM. It is established that the HMM based speaker recognition system requires significantly less amount of data during both during training as well as in testing than GMM based Speaker Recognition System. The performance evaluation of speaker recognition study using robust features for HMM based method and GMM based method is exploited for different mixtures components, training and test durations We demonstrate the speaker recognition studies on TIMIT database.
Keywords: hidden Markov models (HMMs), Gaussian Mixture Model (GMM)), MFCC, Robust Features, Speaker
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ABOUT THE AUTHORS
R. Rajeswara Rao
Prof & Head, Dept. of CSE, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad 75, India
A. Prasad
Prof & Head, Dept. of MCA, Vignan University, Guntur, Andhra Pradesh, India
Ch. Kedari Rao
Asst. Professor, Dept. of cse, DVRCET, Hyderabad, India
R. Rajeswara Rao
Prof & Head, Dept. of CSE, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad 75, India
A. Prasad
Prof & Head, Dept. of MCA, Vignan University, Guntur, Andhra Pradesh, India
Ch. Kedari Rao
Asst. Professor, Dept. of cse, DVRCET, Hyderabad, India
