Combining transcription-based and acoustic-based speaker identifications for broadcast news

Elie Khoury 1, 2 Antoine Laurent 2 Sylvain Meignier 2 Simon Petitrenaud 2
1 IDIAP Research Institute
2 LIUM - Laboratoire d'Informatique de l'Université du Mans
Abstract : In this paper, we consider the issue of speaker identification within audio records of broadcast news. The speaker identity information is extracted from both transcript-based and acoustic-based speaker identification systems. This information is combined in the belief functions framework, which makes coherent the knowledge representation of the problem. The Kuhn-Munkres algorithm is used to optimize the assignment problem of speaker identities and speaker clusters. Experiments carried out on French broadcast news from the French evaluation campaign ESTER show the efficiency of the proposed combination method.
Keywords : Speaker identification speaker diarization belief functions
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012, Kyoto, Japan. pp.4377 - 4380, 2012, Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. 〈10.1109/ICASSP.2012.6288889〉
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Elie Khoury, Antoine Laurent, Sylvain Meignier, Simon Petitrenaud. Combining transcription-based and acoustic-based speaker identifications for broadcast news. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012, Kyoto, Japan. pp.4377 - 4380, 2012, Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. 〈10.1109/ICASSP.2012.6288889〉. 〈hal-01433486〉

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