Open-Set Speaker Identification under Mismatch Conditions

Full text of this paper is not available in the UHRA.This paper presents investigations into the performance of open-set, text-independent speaker identification (OSTI-SI) under mismatched data conditions. The scope of the study includes attempts to reduce the adverse effects of such conditions through the introduction of a modified parallel model combination (PMC) method together with condition-adjusted T-Norm (CT-Norm) into the OSTI-SI framework. The experiments are conducted using examples of real world noise. Based on the outcomes, it is demonstrated that the above approach can lead to considerable improvements in the accuracy of open-set speaker identification operating under severely mismatched data conditions. The paper details the realisation of the modified PMC method and CT-Norm in the context of OSTI-SI, presents the experimental investigations and provides an analysis of the results.otherPeer reviewe

International Workshops of PAAMS 2013, Salamanca, Spain, May 22-24, 2013. Proceedings

This book constitutes the refereed proceedings of the Workshops which complemented the 11th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2013, held in Salamanca, Spain, in May 2013. This volume presents the papers that have been accepted for the workshops: Workshop on Agent-based Approaches for the Transportation Modeling and Optimization, Workshop on Agent-Based Solutions for Manufacturing and Supply Chain, Workshop on User-Centric Technologies and Applications, Workshop on Conflict Resolution in Decision Making, Workshop on Multi-Agent System Based Learning Environments, Workshop on Multi-agent based Applications for Sustainable Energy Systems, Workshop on Agents and multi-agent Systems for AAL and e-Healt

Effectiveness of speaker-dependent feature score pruning in speaker verification

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/ISCCSP.2008.453725

Enhancement of multimodal biometric segregation using unconstrained cohort normalisation

Original article can be found at: http://www.sciencedirect.com/science/journal/00313203 Copyright Elsevier Ltd.This paper presents an investigation into the effects, on the accuracy of multimodal biometrics, of introducing unconstrained cohort normalisation (UCN) into the score-level fusion process. Whilst score normalisation has been widely used in voice biometrics, its effectiveness in other biometrics has not been previously investigated. This study aims to explore the potential usefulness of the said score normalisation technique in face biometrics and to investigate its effectiveness for enhancing the accuracy of multimodal biometrics. The experimental investigations involve the two recognition modes of verification and open-set identification, in clean mixed-quality and degraded data conditions. Based on the experimental results, it is demonstrated that the capabilities provided by UCN can significantly improve the accuracy of fused biometrics. The paper presents the motivation for, and the potential advantages of, the proposed approach and details the experimental study. 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.Peer reviewe

Speaker verification under mismatched data conditions

"This paper is a postprint of a paper submitted to and accepted for publication in IET Signal Processing and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library." [Full text of this article is not available in the UHRA]This study presents investigations into the effectiveness of the state-of-the-art speaker verification techniques (i.e. GMM-UBM and GMM-SVM) in mismatched noise conditions. Based on experiments using white and real world noise, it is shown that the verification performance offered by these methods is severely affected when the level of degradation in the test material is different from that in the training utterances. To address this problem, a modified realisation of the parallel model combination (PMC) method is introduced and a new form of test normalisation (T-norm), termed condition adjusted T-norm, is proposed. It is experimentally demonstrated that the use of these techniques with GMM-UBM can significantly enhance the accuracy in mismatched noise conditions. Based on the experimental results, it is observed that the resultant relative improvement achieved for GMM-UBM (under the most severe mismatch condition considered) is in excess of 70%. Additionally, it is shown that the improvement in the verification accuracy achieved in this way is higher than that obtainable with the direct use of PMC with GMM-UBM. Moreover, it is found that while the accuracy performance of GMM-SVM can also considerably benefit from the use of these techniques, the extensive computational cost involved in this case severely limits the use of such a combined approach in practice.Peer reviewe

Effective speaker verification via dynamic mismatch compensation

This paper presents a new approach to Condition-adjusted T-Norm (CT-Norm) for speaker verification under significant mismatched noise conditions. The study is motivated by the fact that, whilst the standard CT-Norm method offers enhanced accuracy under mismatched data conditions, its effectiveness reduces with the increased severity of such conditions. The proposed approach attempts to address this challenge by providing a more effective reduction of data mismatch through the incorporation of multi-SNR UBMs (universal background models). The effectiveness of the proposed approach is demonstrated through experiments based on examples of real-world noise. It is shown that the superiority of the approach over CT-Norm is particularly significant for such excessive levels of test data degradation considered in the study as 5 dB and below. The paper provides a description of the characteristics of the proposed approach and details the experimental analysis of its effectiveness under different noise conditions.Peer reviewe