The idea of the Speaker Identification is to implement a recognizer using Matlab which can identify a person by processing his/her voice. The basic goal of the paper is to classify and recognize the speeches of different persons. This classification is mainly based on extracting several key features like Mel Frequency Cepstral Coefficients (MFCC) from the speech signals of those persons by using the process of feature extraction using MATLAB. The above features may consists of pitch, amplitude, frequency etc. Using a statistical model like Gaussian mixture model (GMM) and features extracted from those speech signals we build a unique identity for each person who enrolled for speaker recognition. There is an elegant and powerful method for finding the maximum likelihood and that method is called Expectation and Maximization algorithm. The performance of the technique has been measured by three parameters: Number of Speakers in Database, Number of Persons Tested and the % Error.
PRASAD, KAULESHWAR and LOTIA, PIYUSH
"APPLICATION OF GAUSSIAN SUPERVECTOR IN SPEECH ANALYSIS,"
International Journal of Electronics and Electical Engineering: Vol. 3
, Article 14.
Available at: https://www.interscience.in/ijeee/vol3/iss3/14