Performance Evaluation of Statistical Approaches for Text Independent Speaker Recognition Using Source Feature

This paper introduces the performance evaluation of statistical approaches for Text-Independent speaker recognition system using source feature. Linear prediction (LP) residual is used as a representation of excitation information in speech. The speaker-specific information in the excitation of voiced speech is captured using statistical approaches such as Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs). The decrease in the error during training and recognizing speakers during testing phase close to 100% accuracy demonstrates that the excitation component of speech contains speaker-specific information and is indeed being effectively captured by continuous Ergodic HMM than GMM. The performance of the speaker recognition system is evaluated on GMM and 2-state ergodic HMM with different mixture components and test speech duration. We demonstrate the speaker recognition studies on TIMIT database for both GMM and Ergodic HMM.