Mitigate the Reverberant Effects on Speaker Recognition via Multitraining Speaker recognition techniques have been developed into a relatively mature status over the past few decades through continuous research and development work. Existing methods typically use robust features extracted from clean speech signals, and therefore in idealized conditions can achieve very high recognition accuracy. For critical applications, such as security forensics robustness and reliability of the system is crucial. The reverberation condition can be represented by two main parameters namely Reverberation Time (RT) and Direct to Reverberation Ratio (DRR) (which represent the distance of the microphone to the source). This paper presents an efficient method to mitigating or at least alleviates the impacts of reverberation upon speaker verification. Multicondition training approaches are investigated to alleviate such detrimental effects. Three multicondition training methods are then investigated to mitigate such detrimental effects. The first uses matched traintest speaker models based on estimated reverberation time (RT) values. The second utilizes twocondition training where clean and reverberant models are used. Lastly, a fourcondition training setup is proposed and conducted to improve the system performance. The utilized data set building, for SV experiments, training, and speech test material are obtained from the University of Salford Anechoic chamber database (SALUAC). Experimental results show the first and the last types of multicondition training providing significant gains in performance relative to the baseline.

ACOUSTICLABIAL SPEAKER VERIFICATION This paper describes a multimodal approach for speaker verification. The system consists of two classifiers, one using visual features and the other using acoustic features. A lip tracker is used to extract visual information from the speaking face which provides shape and intensity features. We describe an approach for normalizing and mapping different modalities onto a common confidence interval. We also describe a novel method for integrating the scores of multiple classifiers. Verification experiments are reported for the individual modalities and for the combined classifier. The performance of the integrated system outperformed each subsystem and reduced the false acceptance rate of the acoustic subsystem from 2.3% to 0.5%.