18899870
Testing Voice Mimicry with the YOHO Speaker Verification Corpus
The aim of this paper is to determine how vulnerable a speaker verification system is to conscious effort by impostors to mimic a client of the system. The paper explores systematically how much closer an impostor can get to another speaker's voice by repeated attempts. Experiments on 138 speakers in the YOHO database and six people who played a role as imitators showed a fact that professional linguists could successfully attack the system. Non-professional people could have a good chance if they know their closest speaker in the database.
25044094
Generalised Fuzzy Hidden Markov Models for Speech Recognition
A generalised fuzzy approach to statistical modelling techniques for speech recognition is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to hidden Markov models (HMMs). A more robust version of the above fuzzy technique based on the noise clustering (NC) method is also proposed. Experimental results were performed on the TI46 speech data corpus. A significant result for isolatedword recognition performed on a highly confusable vocabulary consisting of the nine English E-set words is that, a 33.8% recognition error rate for the HMM-based system was reduced to 30.5%, 29.9%, 29.8% and 27.8%, respectively, by using the FCM-HMM, the FE-HMM, the NC-FE-HMM, and the NC-FCM-HMM-based systems.
35465720
Reliability score based multimodal fusion for biometric person authentication
In this paper, we propose a robust multilevel fusion strategy involving cascaded fusion of hybrid multimodal fusion of audio-lip-face motion, correlation and depth features for biometric person authentication. The proposed approach combines the information from different audio-video based modules, namely: audio-lip motion module, audio-lip correlation module, 2D+3D motiondepth fusion module, and performs a hybrid cascaded fusion in an automatic, unsupervised and adaptive manner, by adapting to the local performance of each module. This is done by taking the output-score based reliability estimates (confidence measures) of each of the module into account. The module weightings are determined automatically such that the reliability measure of the combined scores is maximised. To test the robustness of the proposed approach, the audio and visual speech (mouth) modalities are degraded to emulate various levels of train/test mismatch; employing additive white Gaussian noise for the audio and JPEG compression for the video signals. The results show improved fusion performance for a range of tested levels of audio and video degradation, compared to the individual module performances. Experiments on a 3D stereovision database AVOZES show that, at severe levels of audio and video mismatch, the audio, mouth, 3D face, and tri-module (audio-lip motion, correlation and depth) fusion EERs were 42.9%, 32%, 15%, and 7.3% respectively for biometric person authentication task.
987011
Similarity normalization for speaker verification by fuzzy fusion
Abstract Similarity or likelihood normalization techniques are important for speaker verification systems as they help to alleviate the variations in the speech signals. In the conventional normalization, the a priori probabilities of the cohort speakers are assumed to be equal. From this standpoint, we apply the theory of fuzzy measure and fuzzy integral to combine the likelihood values of the cohort speakers in which the assumption of equal a priori probabilities is relaxed. This approach replaces the conventional normalization term by the fuzzy integral which acts as a non-linear fusion of the similarity measures of an utterance assigned to the cohort speakers. We illustrate the performance of the proposed approach by testing the speaker verification system with both the conventional and the fuzzy algorithms using the commercial speech corpus TI46. The results in terms of the equal error rates show that the speaker verification system using the fuzzy integral is more flexible and more favorable than the conventional normalization method.
5705405
Robust face-voice based speaker identity verification using multilevel fusion
In this paper, we propose a robust multilevel fusion strategy involving cascaded multimodal fusion of audio-lip-face motion, correlation and depth features for biometric person authentication. The proposed approach combines the information from different audio-video based modules, namely: audio-lip motion module, audio-lip correlation module, 2D+3D motion-depth fusion module, and performs a hybrid cascaded fusion in an automatic, unsupervised and adaptive manner, by adapting to the local performance of each module. This is done by taking the output-score based reliability estimates (confidence measures) of each of the module into account. The module weightings are determined automatically such that the reliability measure of the combined scores is maximised. To test the robustness of the proposed approach, the audio and visual speech (mouth) modalities are degraded to emulate various levels of train/test mismatch; employing additive white Gaussian noise for the audio and JPEG compression for the video signals. The results show improved fusion performance for a range of tested levels of audio and video degradation, compared to the individual module performances. Experiments on a 3D stereovision database AVOZES show that, at severe levels of audio and video mismatch, the audio, mouth, 3D face, and tri-module (audio-lip motion, correlation and depth) fusion EERs were 42.9%, 32%, 15%, and 7.3%, respectively, for biometric person authentication task.
43139712
Speaker Verification with Fuzzy Fusion and Genetic Optimization
578051
Bayesian Biosurveillance of Disease Outbreaks
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.
26946048
Exploiting Spatial and Temporal Locality of Accesses: A New Hardware-Based Monitoring Approach for DSM Systems
The performance of a parallel system with NUMA characteristics depends on the efficient use of local memory accesses. Programming and tool environments for such DSM systems should enable and exploit data locality.
13438707
Interaction in a collaborative augmented reality environment
In this paper we describe an Augmented Reality (AR) system which allows multiple participants to interact with two- and three-dimensional data using tangible user interfaces. Interactively controllable 2D and 3D information is seamless integrated into the system.
31316829
Restoration of the Sphere-Cortex Homeomorphism
The proposed algorithm has been developed as a pre-processing tool for inflating cortical surface meshes, which have been created using segmentation and subsequent triangulation of magnetic resonance images (MRI) [1]. It works directly on the triangulated surface and is therefore completely independent from the underlying segmentation. It needs no other information than the triangle mesh itself, which makes it generally applicable for the removal of topological noise. The homeomorphism between cortical surface and sphere is re-established by removing handles and opening connections. Moreover, the presented approach guarantees a manifold mesh by locally examining connectivity in the neighbourhood of each vertex and removing non-manifold components. It will be embedded into the source reconstruction software package CURRY (Compumedics Neuroscan, El Paso, TX, USA).
37039344
An e-Learning Tool for the Austrian Citizen Card
Austria is one of the leading countries in the field of electronic identity. The Austrian Citizen Card System is presently going live; the next step is to inform the citizen about the functionalities and advantages, as well as on how to use the card. On authority of the Federal Chancellor’s Office, the Danube University has developed an e-learning tool that will be made available to all Austrian households. The target group is the citizen rather than the early technological adopter, and the aim is to explain the use of a citizen card rather than the technical background. The main pedagogical method therefore is to reduce technical complexity to an absolute minimum. This e-learning tool is positioned to be one piece of a sophisticated e-government communication strategy.