Speaker diarization using one-class support vector machines
Speech Communication
The LIA RT'07 Speaker Diarization System
Multimodal Technologies for Perception of Humans
An Adaptive BIC Approach for Robust Speaker Change Detection in Continuous Audio Streams
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Fusion of Acoustic and Prosodic Features for Speaker Clustering
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
An information theoretic approach to speaker diarization of meeting data
IEEE Transactions on Audio, Speech, and Language Processing
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Technical improvements of the E-HMM based speaker diarization system for meeting records
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
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Seeking within a speech sequence the speaker utterances is one of the main tasks of indexing. In this paper, the proposed speaker tracking system is defined in the case where all speaker identities are known beforehand. The conversation is modeled as an evolutive HMM-like model, in which speaker models computed are added one by one. A temporary indexing is proposed after each speaker adding and then challenged at the next step. This process is iterated until all the speakers are detected. The system has been assessed using multi-speaker messages generated by concatenation of Switchboard mono-speaker segments. The obtained results show the potentiality of the proposed solution.