Hybridization process for text-independent speaker identification based on vector quantization model

Abstract

This paper examines performances of an independent Speaker Identification System (SIS) based on a template model using a Vector Quantization (VQ) method. Template model is characterized by the implementation platform based on a comparison process where the speaker model with the smallest distortion score is identified. In order to analyze the decision of the system and its confidence, a thresholding decision was introduced as a verdict condition. Thus, a new notion around decision quality was performed. Moreover, this threshold returns a discriminative criterion for selecting the training models used in the matching process and clustering with a second SIS will be allowed. According to the results, it was concluded as through the use of the proposed method; the desired performance was reached. As fulfillment, we have been able to custom a Hybridization process based on SIS-VQ model.