A Novel Method for Text-Independent Speaker Identification Using MFCC and GMM
The area of speaker recognition is concerned with extracting the identity of the person speaking. Speaker recognition can be classified into speaker identification and speaker verification. Speaker identification can be Text-Independent or Text-Dependent. In this paper we lay emphasis on text-Independent speaker identification system where we adopted Mel-Frequency Cepstral Coefficients (MFCC) as the speaker speech feature parameters in the system and the concept of Gaussian Mixture Modeling (GMM) for modeling the extracted speech feature. We used the Maximum Likelihood Ratio Detector algorithm for the decision making process. The experimental study has been performed for various speech time duration and several languages and was conducted around MATLAB 7 language environment. Gaussian mixture speaker model attains high recognition rate for various speech durations.
M.S.Sinith Anoop Salim Gowri Sankar K Sandeep Narayanan K V Vishnu Soman
Dept. of Electronics and Communication Engineering,Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India
国际会议
上海
英文
292-296
2010-10-20（万方平台首次上网日期，不代表论文的发表时间）