Abstract:
Principal Component analysis (PCA) is useful in identifying patterns in data, and expressing data in a manner which highlights their similarities and differences. This concept was extracted to reduce high dimensional Mel�s Frequency Cepstral Coefficients (MFCC) into low dimensional feature vectors. Since MFCC�s are high in Dimensions And Truncation Of These Dependent Coefficients May Lead To Error In Identification Of Speaker�s Speech Recognition. In This Paper Text Independent Speaker Identification Model Is Developed By Combining MFCC�s With PCA To Obtain Compressed Feature Vectors Without Losing Much Information. Generalized Gaussian Mixture Model (GGMM) Was Used As Modeling Techniques By Assuming The New Feature Vectors Follows (GGMM) [Reynolds, (1995)] [7]. The Experiment Was Done With 40 Speakers With 10 Utterances Of Each Speaker Locally Recorded Database.

Keywords:Principal Component Analysis