Likelihood Ratio Based Score Fusion for Audio-Visual Speaker Identification in Challenging Environment

Abstract

It is well known to enhance the performance of noise robust speaker identification using visual speech information with audio utterances. This paper presents an approach to evaluate the performance of a noise robust audio-visual speaker identification system using likelihood ratio based score fusion in challenging environment. Though the traditional HMM based audio-visual speaker identification system is very sensitive to the speech parameter variation, the proposed likelihood ratio based score fusion method is found to be stance and performs well for improving the robustness and naturalness of human-computerinteraction. In this paper, we investigate the proposed audiovisual speaker identification system in typical office environments conditions. To do this, we investigated two approaches that utilize speech utterance with visual features to improve speaker identification performance in acoustically and visually challenging environment: one seeks to eliminate the noise from the acoustic and visual features by using speech and facial image pre-processing techniques. The other task combines speech and facial features that have been used by the multiple Discrete Hidden Markov Model classifiers with likelihood ratio based score fusion. It is shown that the proposed system can improve a significant amount of performance for audio-visual speaker identification in challenging official environment conditions.

An Overview on Multi-biometric Score-level Fusion - Verification and Identification

“…Given those vectors, a classifier is trained to classify a new given vector into genuine or imposter class. Different types of classifiers can be used, just as neural networks (Alsaade, 2010), K-NN (Jin et al, 2004), SVM (Singh et al, 2007;Garcia-salicetti et al, 2005), Adaboost (Ichino et al, 2010;Moin and Parviz, 2009), or as likelihood ratio (Nandakumar et al, 2008;Islam and Rahman, 2010) classifiers. Some works showed comparable results between combination rules and classification based fusion (Rodríguez et al, 2008;Mehrotra et al, 2012).…”