Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Robust speaker diarization for meetings: ICSI RT06S meetings evaluation system
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
The SRI-ICSI Spring 2007 Meeting and Lecture Recognition System
Multimodal Technologies for Perception of Humans
Narrative theme navigation for sitcoms supported by fan-generated scripts
Proceedings of the 3rd international workshop on Automated information extraction in media production
Towards automatic speaker retrieval for large multimedia archives
Proceedings of the 3rd international workshop on Automated information extraction in media production
Tuning-robust initialization methods for speaker diarization
IEEE Transactions on Audio, Speech, and Language Processing
Speaker diarization exploiting the eigengap criterion and cluster ensembles
IEEE Transactions on Audio, Speech, and Language Processing
Narrative theme navigation for sitcoms supported by fan-generated scripts
Multimedia Tools and Applications
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In this paper, we present the ICSI speaker diarization system. This system was used in the 2007 National Institute of Standards and Technology (NIST) Rich Transcription evaluation. The ICSI system automatically performs both speaker segmentation and clustering without any prior knowledge of the identities or the number of speakers. Our system uses "standard" speech processing components and techniques such as HMMs, agglomerative clustering, and the Bayesian Information Criterion. However, we have developed the system with an eye towards robustness and ease of portability. Thus we have avoided the use of any sort of model that requires training on "outside" data and we have attempted to develop algorithms that require as little tuning as possible.The system is simular to last year's system [1] except for three aspects. We used the most recent available version of the beam-forming toolkit, we implemented a new speech/non-speech detector that does not require models trained on meeting data and we performed our development on a much larger set of recordings.