ARTICLE Vijayasenan_TASL_2011/IDIAP An Information Theoretic Combination of MFCC and TDOA Features for Speaker Diarization Vijayasenan, Deepu Valente, Fabio Bourlard, Hervé http://publications.idiap.ch/index.php/publications/showcite/Vijayasenan_Idiap-RR-22-2010 Related documents IEEE Transactions on Audio Speech and Language Processing 19 2 431-438 2011 February 2011 10.1109/TASL.2010.2048603 doi This correspondence describes a novel system for speaker diarization of meetings recordings based on the combination of acoustic features (MFCC) and time delay of arrivals (TDOAS). The first part of the paper analyzes differences between MFCC and TDOA features which possess completely different statistical properties. When Gaussian mixture models are used, experiments reveal that the diarization system is sensitive to the different recording scenarios (i.e., meeting rooms with varying number of microphones). In the second part, a new multistream diarization system is proposed extending previous work on information theoretic diarization. Both speaker clustering and speaker realignment steps are discussed; in contrary to current systems, the proposed method avoids to perform the feature combination averaging log-likelihood scores. Experiments on meetings data reveal that the proposed approach outperforms the GMM-based system when the recording is done with varying number of microphones. REPORT Vijayasenan_Idiap-RR-22-2010/IDIAP An Information Theoretic Combination of MFCC and TDOA Features for Speaker Diarization Vijayasenan, Deepu Valente, Fabio Bourlard, Hervé Idiap-RR-22-2010 2010 Idiap July 2010 This work describes a novel system for speaker diarization of meetings recordings based on the combination of acoustic features (MFCC) and Time Delay of Arrivals (TDOA). The first part of the paper analyzes differences between MFCC and TDOA features which possess completely different statistical properties. When Gaussian Mixture Models are used, experiments reveal that the diarization system is sensitive to the different recording scenarios (i.e. meeting rooms with varying number of microphones). In the second part, a new multistream diarization system is proposed extending previous work on Information Theoretic diarization. Both speaker clustering and speaker realignment steps are discussed; in contrary to current systems, the proposed method avoids to perform the feature combination averaging log-likelihood scores. Experiments on meetings data reveal that the proposed approach outperforms the GMM based system when the recording is done with varying number of microphones.