CONF
Imseng_ICASSP_2010/IDIAP
An Adaptive Initialization Method for Speaker Diarization based on Prosodic Features
Imseng, David
Friedland, Gerald
Gaussian Mixture Models
Prosodic features
Speaker Diarization
EXTERNAL
https://publications.idiap.ch/attachments/papers/2010/Imseng_ICASSP_2010.pdf
PUBLIC
https://publications.idiap.ch/index.php/publications/showcite/Imseng_Idiap-RR-02-2010
Related documents
Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing
Dallas, USA
2010
March 2010
4946-4949
The following article presents a novel, adaptive initialization scheme that can be applied to most state-ofthe-art Speaker Diarization algorithms, i.e. algorithms that use agglomerative hierarchical clustering with Bayesian Information Criterion (BIC) and Gaussian Mixture Models (GMMs) of frame-based cepstral features (MFCCs). The initialization method is a combination of the recently proposed “adaptive seconds per Gaussian†(ASPG) method and a new pre-clustering and number of initial clusters estimation method based on prosodic features. The presented initialization method has two important advantages. First, the method requires no manual tuning and is robust against file length and speaker count variations. Second, the method outperforms our previously used initialization methods on all benchmark files that were presented in the 2006, 2007, and 2009 NIST Rich Transcription (RT) evaluations and results in a Diarization Error Rate (DER) improvement of up to 67% (relative).
REPORT
Imseng_Idiap-RR-02-2010/IDIAP
An Adaptive Initialization Method for Speaker Diarization based on Prosodic Features
Imseng, David
Friedland, Gerald
EXTERNAL
https://publications.idiap.ch/attachments/reports/2009/Imseng_Idiap-RR-02-2010.pdf
PUBLIC
Idiap-RR-02-2010
2010
Idiap
January 2010
The following article presents a novel, adaptive initialization scheme that can be applied to most state-ofthe-art Speaker Diarization algorithms, i.e. algorithms that use agglomerative hierarchical clustering with Bayesian Information Criterion (BIC) and Gaussian Mixture Models (GMMs) of frame-based cepstral features (MFCCs). The initialization method is a combination of the recently proposed “adaptive seconds per Gaussian†(ASPG) method and a new pre-clustering and number of initial clusters estimation method based on prosodic features. The presented initialization method has two important advantages. First, the method requires no manual tuning and is robust against file length and speaker count variations. Second, the method outperforms our previously used initialization methods on all benchmark files that were presented in the 2006, 2007, and 2009 NIST Rich Transcription (RT) evaluations and results in a Diarization Error Rate (DER) improvement of up to 67% (relative).