Effectiveness in the Realisation of Speaker Authentication

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.An important consideration for the deployment of speaker recognition in authentication applications is the approach to the formation of training and testing utterances . Whilst defining this for a specific scenario is influenced by the associated requirements and conditions, the process can be further guided through the establishment of the relative usefulness of alternative frameworks for composing the training and testing material. In this regard, the present paper provides an analysis of the effects, on the speaker recognition accuracy, of various bases for the formation of the training and testing data. The experimental investigations are conducted based on the use of digit utterances taken from the XM2VTS database. The paper presents a detailed description of the individual approaches considered and discusses the experimental results obtained in different cases

Shape and Texture Combined Face Recognition for Detection of Forged ID Documents

This paper proposes a face recognition system that can be used to effectively match a face image scanned from an identity (ID) doc-ument against the face image stored in the biometric chip of such a document. The purpose of this specific face recognition algorithm is to aid the automatic detection of forged ID documents where the photography printed on the document’s surface has been altered or replaced. The proposed algorithm uses a novel combination of texture and shape features together with sub-space representation techniques. In addition, the robustness of the proposed algorithm when dealing with more general face recognition tasks has been proven with the Good, the Bad & the Ugly (GBU) dataset, one of the most challenging datasets containing frontal faces. The proposed algorithm has been complement-ed with a novel method that adopts two operating points to enhance the reliability of the algorithm’s final verification decision.Final Accepted Versio

Effectiveness in Open-Set Speaker Identification

This paper presents investigations into the relative effectiveness of two alternative approaches to open-set text-independent speaker identification (OSTI-SI). The methods considered are the recently introduced i-vector and the more traditional GMM-UBM method supported by score normalisation. The study is motivated by the growing need for effective extraction of intelligence and evidence from audio recordings in the fight against crime. OSTI-SI is known to be the most challenging subclass of speaker recognition, and its adoption in criminal investigation applications is further complicated by undesired variations in speech characteristics due to changing levels of environmental noise. In this study, the experimental investigations are conducted using a protocol developed for the identification task, based on the NIST speaker recognition evaluation corpus of 2008. In order to closely cover relevant conditions in the considered application areas and investigate the identification performance in such scenarios, the speech data is contaminated with a range of real-world noise. The paper provides a detailed description of the experimental study and presents a thorough analysis of the results.Peer reviewe

Pass Phrase Based Speaker Recognition for Authentication

Speaker recognition in applications of our daily lives is not yet in widespread use. In order for biometric technology to make sense for real-world authentication applications and be accepted by end users, convenience of use, robustness and accuracy of such a system are equally important. This paper defines these requirements for pass phrase based voice authentication embedded within a multi modal biometric system and describes methods and algorithms developed and optimized for the demands of such an application. Classification is based on dynamic time warping which can cope with limited training data. MFCC features which have been optimized for speaker specific properties are used. Robustness of the system is increased with speech enhancement and cepstral mean subtraction. Furthermore, vector quantization with speaker specific codebooks is applied in order to decrease storage requirements for the biometric template. On an appropriate data base, a verification EER of 2.7% is achieved with limited training and test material

Open-set speaker identification with diverse-duration speech data

Rawande Karadaghi, Heinz Hertlein, and, Aladdin Ariyaeeinia, "Open-set speaker identification with diverse-duration speech data", in Proceedings of SPIE 9457, Biometric and Surveillance Technology for Human Activity Identification XII, Baltimore, USA, 20 April 2015. DOI:10.1117/12.217633

Pass Phrase Based Speaker Recognition for Authentication

Abstract: Speaker recognition in applications of our daily lives is not yet in widespread use. In order for biometric technology to make sense for real-world authentication applications and be accepted by end users, convenience of use, robustness and accuracy of such a system are equally important. This paper defines these requirements for pass phrase based voice authentication embedded within a multi modal biometric system and describes methods and algorithms developed and optimized for the demands of such an application. Classification is based on dynamic time warping which can cope with limited training data. MFCC features which have been optimized for speaker specific properties are used. Robustness of the system is increased with speech enhancement and cepstral mean subtraction. Furthermore, vector quantization with speaker specific codebooks is applied in order to decrease storage requirements for the biometric template. On an appropriate data base, a verification EER of 2.7 % is achieved with limited training and test material. 1 Introduction: Speake

Engineering Concurrent Software Guided by Statistical Performance Analysis

This paper introduces the ADVANCE approach to engineering concurrent systems using a new component-based approach. A cost-directed tool-chain maps concurrent programs onto emerging hardware architectures, where costs are expressed in terms of programmer annotations for the throughput, latency and jitter of components. These are then synthesized using advanced statistical analysis techniques to give overall cost information about the concurrent system that can be exploited by the hardware virtualisation layer to drive mapping and scheduling decisions. Initial performance results are presented, showing that the ADVANCE technologies provide a promising approach to dealing with near- and future-term complexities of programming heterogeneous multi-core systems