Abstract
Performances of speaker verification systems are superb in clean noise-free conditions but the reliability of the systems drop severely in noisy environments. In this study, we propose a novel approach by introducing Support Vector Machine (SVM) as indicator system for audio reliability estimation. This approach directly validate the quality of the incoming (claimant) speech signal so as to adaptively change the weighting factor for fusion of both subsystem scores. The effectiveness of this approach has been experimented to a multibiometric verification system that employs lipreading images as visual features. This verification system uses SVM as a classifier for both subsystems. Principle Component Analysis (PCA) technique is executed for visual features extraction while for the audio feature extraction; Linear Predictive Coding (LPC) technique has been utilized. In this study, we found that the SVM indicator system is able to determine the quality of the speech signal up to 99.66%. At 10 dB SNR, EER performances are observed as 51.13%, 9.3%, and 0.27% for audio only system, fixed weighting system and adaptive weighting system, respectively.
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Ramli, D.A., Samad, S.A., Hussain, A. (2010). An Adaptive Multibiometric System for Uncertain Audio Condition. In: Ao, SI., Gelman, L. (eds) Electronic Engineering and Computing Technology. Lecture Notes in Electrical Engineering, vol 60. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8776-8_15
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