Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Progress in the AMIDA Speaker Diarization System for Meeting Data
Multimodal Technologies for Perception of Humans
Multimodal Technologies for Perception of Humans
The ICSI RT07s Speaker Diarization System
Multimodal Technologies for Perception of Humans
The LIA RT'07 Speaker Diarization System
Multimodal Technologies for Perception of Humans
Multi-stage Speaker Diarization for Conference and Lecture Meetings
Multimodal Technologies for Perception of Humans
Speaker Diarization for Conference Room: The UPC RT07s Evaluation System
Multimodal Technologies for Perception of Humans
Live speaker identification in conversations
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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
Acoustic Beamforming for Speaker Diarization of Meetings
IEEE Transactions on Audio, Speech, and Language Processing
Prosodic and other Long-Term Features for Speaker Diarization
IEEE Transactions on Audio, Speech, and Language Processing
A review on speaker diarization systems and approaches
Speech Communication
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This paper investigates a typical speaker diarization system regarding its robustness against initialization parameter variation and presents a method to reduce manual tuning of these values significantly. The behavior of an agglomerative hierarchical clustering system is studied to determine which initialization parameters impact accuracy most. We show that the accuracy of typical systems is indeed very sensitive to the values chosen for the initialization parameters and factors such as the duration of speech in the recording. We then present a solution that reduces the sensitivity of the initialization values and therefore reduces the need for manual tuning significantly while at the same time increasing the accuracy of the system. For short meetings extracted from the previous (2006, 2007, and 2009) National Institute of Standards and Technology (NIST) Rich Transcription (RT) evaluation data, the decrease of the diarization error rate is up to 50% relative. The approach consists of a novel initialization parameter estimation method for speaker diarization that uses agglomerative clustering with Bayesian information criterion (BIC) and Gaussian mixture models (GMMs) of frame-based cepstral features (MFCCs). The estimation method balances the relationship between the optimal value of the seconds of speech data per Gaussian and the duration of the speech data and is combined with a novel nonuniform initialization method. This approach results in a system that performs better than the current ICSI baseline engine on datasets of the NIST RT evaluations of the years 2006, 2007, and 2009.