DISTBIC: a speaker-based segmentation for audio data indexing
Speech Communication - Special issue on accessing information in spoken audio
The rich transcription 2005 spring meeting recognition evaluation
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Robust speaker segmentation for meetings: the ICSI-SRI spring 2005 diarization system
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Progress in the AMIDA Speaker Diarization System for Meeting Data
Multimodal Technologies for Perception of Humans
Robust speaker diarization for meetings: ICSI RT06S meetings evaluation system
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
A review on speaker diarization systems and approaches
Speech Communication
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The TNO speaker speaker diarization system is based on a standard BIC segmentation and clustering algorithm. Since for the NIST Rich Transcription speaker dizarization evaluation measure correct speech detection appears to be essential, we have developed a speech activity detector (SAD) as well. This is based on decoding the speech signal using two Gaussian Mixture Models trained on silence and speech. The SAD was trained on only AMI development test data, and performed quite well in the evaluation on all 5 meeting locations, with a SAD error rate of 5.0 %. For the speaker clustering algorithm we optimized the BIC penalty parameter λ to 14, which is quite high with respect to the theoretical value of 1. The final speaker diarization error rate was evaluated at 35.1 %.