Noise Compensation in a Multi-Modal Verification System

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Author(s)
Sanderson, C
Paliwal, KK
Griffith University Author(s)
Year published
2001
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In this paper we propose an adaptive multi-modal verification system comprised of a modified Minimum Cost Bayesian Classifier (MCBC) and a method to find the reliability of the speech expert for various noisy conditions. The modified MCBC takes into account the reliability of each modality expert, allowing the de-emphasis of the contribution of opinions from the expert affected by noise. Reliability of the speech expert is found without directly modeling the noisy speech or finding the reliability a priori for various conditions of the speech signal. Experiments on the Digit Database show the Total Error (TE) to ...
View more >In this paper we propose an adaptive multi-modal verification system comprised of a modified Minimum Cost Bayesian Classifier (MCBC) and a method to find the reliability of the speech expert for various noisy conditions. The modified MCBC takes into account the reliability of each modality expert, allowing the de-emphasis of the contribution of opinions from the expert affected by noise. Reliability of the speech expert is found without directly modeling the noisy speech or finding the reliability a priori for various conditions of the speech signal. Experiments on the Digit Database show the Total Error (TE) to be reduced by 78% when compared to a non-adaptive system.
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View more >In this paper we propose an adaptive multi-modal verification system comprised of a modified Minimum Cost Bayesian Classifier (MCBC) and a method to find the reliability of the speech expert for various noisy conditions. The modified MCBC takes into account the reliability of each modality expert, allowing the de-emphasis of the contribution of opinions from the expert affected by noise. Reliability of the speech expert is found without directly modeling the noisy speech or finding the reliability a priori for various conditions of the speech signal. Experiments on the Digit Database show the Total Error (TE) to be reduced by 78% when compared to a non-adaptive system.
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Conference Title
2001 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-VI, PROCEEDINGS
Volume
1
Copyright Statement
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