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 Eylül 2009, ss.39-42 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Basıldığı Şehir: Famagusta
  • Basıldığı Ülke: CYPRUS
  • Sayfa Sayıları: ss.39-42
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

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.