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Overlapping speech has been identified as one of the main sources of errors in diarization of meeting room conversations. Therefore, overlap detection has become an important step prior to speaker diarization. Studies on conversational analysis have shown ...
Speaker diarization is the task of identifying ``who spoke when'' in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization sys ...
Speaker diarization of meetings can be significantly improved by overlap handling. Several previous works have explored the use of different features such as spectral, spatial and energy for overlap detection. This paper proposes a method to estimate proba ...
Improved diarization results can be obtained through combination of multiple systems. Several combination techniques have been proposed based on output voting, initialization and also integrated approaches. This paper proposes and investigates a novel appr ...
Overlapping speech is a source of significant errors in speaker diarization of spontaneous meeting recordings. Recent works on speaker diarization have attempted to solve the problem of overlap detection using classifiers trained on acoustic and spatial fe ...
Speaker diarization is the task of identifying “who spoke when” in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization syste ...
