In speaker recognition tasks, the main reason for reduced accuracy is due to closely resembling speakers in the acoustic space. Conventional GMM-based modelling technique captures unique features along with common features among various classes. Further, it ignores knowledge of phonetic content of the speech. 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 closely 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 identification task. Experiments have been conducted on speaker identification task using speech data of 192 female speakers from TIMIT corpus.The performance of the proposed system is compared with that of a conventional GMM-based technique and a significant improvement is noted.
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