206844132
Information based speaker verification
We discuss the conceptual and computational frameworks of information theory for decision making in speaker verification. The proposed approach departs from other conventional scoring models for speaker verification as the first approach takes into account the quantity of 'surprise' or information content. We compare the new approach with a widely used log-likelihood normalization method for speaker verification. Experimental results on a commercial speech corpus validates the theoretical foundation of the proposed method. Furthermore, we introduce the unique entropic measure of uncertainty in the verification scoring.
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.
43139712
Speaker Verification with Fuzzy Fusion and Genetic Optimization
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.
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.
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.
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.
47337297
probeBase: an online resource for rRNA-targeted oligonucleotide probes
Ribosomal RNA-(rRNA)-targeted oligonucleotide probes are widely used for culture-independent identification of microorganisms in environmental and clinical samples. ProbeBase is a comprehensive database containing more than 700 published rRNA-targeted oligonucleotide probe sequences (status August 2002) with supporting bibliographic and biological annotation that can be accessed through the internet at http://www.probebase.net. Each oligonucleotide probe entry contains information on target organisms, target molecule (small- or large-subunit rRNA) and position, G+C content, predicted melting temperature, molecular weight, necessity of competitor probes, and the reference that originally described the oligonucleotide probe, including a link to the respective abstract at PubMed. In addition, probes successfully used for fluorescence in situ hybridization (FISH) are highlighted and the recommended hybridization conditions are listed. ProbeBase also offers difference alignments for 16S rRNA-targeted probes by using the probe match tool of the ARB software and the latest small-subunit rRNA ARB database (release June 2002). The option to directly submit probe sequences to the probe match tool of the Ribosomal Database Project II (RDP-II) further allows one to extract supplementary information on probe specificities. The two main features of probeBase, 'search probeBase' and 'find probe set', help researchers to find suitable, published oligonucleotide probes for microorganisms of interest or for rRNA gene sequences submitted by the user. Furthermore, the 'search target site' option provides guidance for the development of new FISH probes.
1173406
Geschäftsprozeßgestaltung durch Kommunikation und Information
13925823
Context inference of users' social relationships and distributed policy management
Inference of high-level context is becoming crucial in development of context-aware applications. An example is social context inference - i.e., deriving social relations based upon the user's daily communication with other people. The efficiency of this mechanism mainly depends on the method(s) used to draw inferences based on existing evidence and sample information, such as a training data. Our approach uses rule-based data mining, Bayesian network inference, and user feedback to compute the probabilities of another user being in the specific social relationship with a user whose daily communication is logged by a mobile phone. In addition, a privacy mechanism is required to ensure the user's personal integrity and privacy when sharing this user's sensitive context data. Therefore, the derived social relations are used to define a user's policies for context access control, which grant the restricted context information scope depending on the user's current context. Finally, we propose a distributed architecture capable of managing this context information based upon these context access policies.
13891540
MagicMeeting: A Collaborative Tangible Augmented Reality System
We describe an augmented reality (AR) system that allows multiple participants to interact with 2D and 3D data using tangible user interfaces. The system features face-to-face communication, collaborative viewing and manipulation of 3D models, and seamless access to 2D desktop applications within the shared 3D space. All virtual content, including 3D models and 2D desktop windows, is attached to tracked physical objects in order to leverage the efficiencies of natural two-handed manipulation. The presence of 2D desktop space within 3D facilitates data exchange between the two realms, enables control of 3D information by 2D applications, and generally increases productivity by providing access to familiar tools. We present a general concept for a collaborative tangible AR system, including a comprehensive set of interaction techniques, a distributed hardware setup, and a component-based software architecture that can be flexibly configured using XML. We show the validity of our concept with an implementation of an application scenario from the automotive industry.