Multidimensional scaling for fast speaker clustering

Chi Chun Hsia, Kuo Yuan Lee, Chih Chieh Chuang, Yu-Hsien Chiu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This study presents a fast speaker clustering method based on multidimensional scaling. Speech segments are trained as initial acoustic models. MDS is utilized to transform acoustic models to a space with the coordinate best preserve the distances or dissimilarity between models. Speaker clusters are clustered using vector quantization on the MDS coordinates and the acoustic speaker models are trained on MFCCs features for each cluster. Experimental results show the proposed method outperforms the baseline speaker clustering method in lower execution time.

Original languageEnglish
Title of host publication2010 7th International Symposium on Chinese Spoken Language Processing, ISCSLP 2010 - Proceedings
Pages296-299
Number of pages4
DOIs
Publication statusPublished - 2010 Dec 1
Event2010 7th International Symposium on Chinese Spoken Language Processing, ISCSLP 2010 - Tainan, Taiwan
Duration: 2010 Nov 292010 Dec 3

Publication series

Name2010 7th International Symposium on Chinese Spoken Language Processing, ISCSLP 2010 - Proceedings

Other

Other2010 7th International Symposium on Chinese Spoken Language Processing, ISCSLP 2010
CountryTaiwan
CityTainan
Period10-11-2910-12-03

All Science Journal Classification (ASJC) codes

  • Linguistics and Language

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