Speaker diarization in meeting audio
Tin Lay Nwe, Hanwu Sun, Haizhou Li, Susanto Rahardja · 2009
This paper describes speaker diarization system on a NIST Rich Transcription 2007 (RT-07) meeting recognition evaluation data set for the task of multiple distant microphone (MDM). Our implementation includes three components: initial clustering, non-speech removal and cluster purification. Initial clusters are generated using directional of arrival (DOA) information and bootstrap clustering. Multiple GMM modeling for speech/non-speech classification is employed for non-speech removal component. In addition, a novel system fusion strategy using information from receiver operating curve (ROC) is proposed for non-speech removal component. Finally, consensus clustering approach together with iterative GMM clustering method is employed for speaker cluster purification. The system achieves the overall DER of 10.81%.