E-HMM approach for learning and adapting sound models for speaker indexing

  title={E-HMM approach for learning and adapting sound models for speaker indexing},
  author={Sylvain Meignier and Jean-François Bonastre and St{\'e}phane Igounet},
This paper presents an iterative process for blind speaker indexing based on a HMM. This process detects and adds speakers one after the other to the evolutive HMM (E-HMM). The use of this HMM approach takes advantage of the different components of AMIRAL automatic speaker recognition system (ASR system: frontend processing, learning, loglikelihood ratio computing) from LIA. The proposed solution reduces the miss detection of short utterances by exploiting all the information (detected speakers… CONTINUE READING
Highly Cited
This paper has 78 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 53 extracted citations

78 Citations

Citations per Year
Semantic Scholar estimates that this publication has 78 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-8 of 8 references

Overview of the ELISA consortium research

  • Elisa Consortium
  • activities, Odyssey,
  • 2001
1 Excerpt

Use of second order statistic for speaker-based segmentation, EUROSPEECH

  • P. Delacourt, D. Kryze, C. J. Wellekens
  • 1999
2 Excerpts

Segregation of speakers for speech recognition and speaker identification

  • H. Gish, H-H Siu, R. Rohlicek
  • 1991
1 Excerpt

Similar Papers

Loading similar papers…