GMM and i-vector based speaker verification using speaker-specific-text for short utterances

In speaker recognition tasks, one of the reasons for reduced accuracy is due to closely resembling speakers in the acoustic space. In order to increase the discriminative power of the classifier, the system must be able to use only the unique features of a given speaker with respect to his/her acoustically resembling speaker. This paper proposes a technique to reduce the confusion errors, by finding speaker-specific phonemes and formulate a text using the subset of phonemes that are unique, for speaker verification task using GMM-based approach and i-vector based approach. Experiments have been conducted on speaker verification task using speech data of 50 speakers collected in a laboratory environment. The experiments show that the Equal Error Rate (EER) has been decreased by 4% and 4.5% using speaker-specific-text when compared to conventional GMM and base line i-vector based technique respectively.