Text independent Voice authentication using GMM (Gaussian Mixture Model)

Post date: Oct 05, 2014 1:25:29 PM

An efficient, and intuitive algorithm is presented for the identification of speakers from a long dataset (like YouTube long discussion, Cocktail party recorded audio or video).The goal of automatic speaker identification is to identify the number of different speakers and prepare a model for that speaker by extraction, characterization and speaker-specific information contained in the speech signal. Speaker identification is an area with many different applications. The most practical uses can be found in areas such as security, surveillance, and automatic transcription in a multi-speaker environment. We have tried to obtained 85 ~ 95% of accuracy using speaker modelling of vector quantization and Gaussian Mixture model ,so by doing various number of experiments we are able to obtain 79 ~ 82% of identification rate using Vector quantization and 85 ~ 92.6% of identification rate using GMM modelling by Expectation maximization parameter estimation depending on variation of parameter.