Principal Component Based Classification for Text-Independent Speaker Identification


Hanilci C., ERTAŞ F.

5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, Famagusta, CYPRUS, 2 - 04 September 2009, pp.39-42 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • City: Famagusta
  • Country: CYPRUS
  • Page Numbers: pp.39-42

Abstract

Classification based on Principal Component analysis has recently appeared in the literature in application to text-independent speaker identification. However, results have been reported for only clean speech data. In this paper, we evaluate the performance of principal component classifier for text-independent speaker identification on telephone speech. We then improve its identification performance using a Vector Quantization classifier in combination, through fusion of classifier scores. An identification rate of 78.27% has been obtained on the NTIMIT database, which is well above the best identification rate ever reported in the literature obtained by using only one type of feature set.