Online Speaker Clustering Using Incremental Learning of an Ergodic Hidden Markov Model-Reference-Cited by-同舟云学术

Online Speaker Clustering Using Incremental Learning of an Ergodic Hidden Markov Model

Published:2012 Issue:10 Volume:E95.D Page:2469-2478
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

KOSHINAKA Takafumi¹²,NAGATOMO Kentaro¹,SHINODA Koichi²

Affiliation:

1. Information and Media Processing Labs., NEC Corporation

2. Department of Computer Science, Tokyo Institute of Technology

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E95.D/10/E95.D_2469/_pdf

Reference26 articles.

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2. [2] T. Hain, S.E. Johnson, A. Tuerk, P.C. Woodland, and S. Young, “Segment generation and clustering in the HTK broadcast news transcription system,” Proc. DARPA Broadcast News Transcription and Understanding Workshop, pp.133-137, 1998.

3. [3] H. Jin, F. Kubala, and R. Schwartz, “Automatic speaker clustering,” Proc. DARPA Speech Recognition Workshop, pp.108-111, 1997.

4. [4] M.A. Siegler, U. Jain, B. Raj, and R.M. Stern, “Automatic segmentation classification and clustering of broadcast news audio,” Proc. DARPA Speech Recognition Workshop, pp.97-99, 1997.

5. [5] M. Collet, D. Charlet, and F. Bimbot, “Speaker tracking by anchor models using speaker segment cluster information,” Proc. ICASSP2006, vol.I, pp.1009-1012, 2006.

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