10674232
Audio Visual Speaker Verification Based on Hybrid Fusion of Cross Modal Features
In this paper, we propose hybrid fusion of audio and explicit correlation features for speaker identity verification applications. Experiments were performed with the GMM based speaker models with a hybrid fusion technique involving late fusion of explicit cross-modal fusion features, with implicit eigen lip and audio MFCC features. An evaluation of the system performance with different gender specific datasets from controlled VidTIMIT data base and opportunistic UCBN database shows a significant performance improvement.
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.
43139712
Speaker Verification with Fuzzy Fusion and Genetic Optimization
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.
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.
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.
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.
751960
Performance Evaluation Methodologies for Abstract Data Type Implementation Techniques
Abstract The runtime management system for a programming language must allow the efficient manipulation of complex objects, such as instances of abstract data types (ADTs). We describe an implementation model for ADTs based on coroutine objects (i.e. as an alternative to ad hoc modifications of the runtime stack). We evaluate the performances of variants of our implementation model using two different approaches: a benchmarking technique which monitors actual costs for test programs and a “pseudo-execution” technique which estimates costs based on idealized instruction timings. The results of our comparison indicate that the two forms of evaluation give very similar cost estimates, supporting the use of the (cheaper) pseudo-execution technique. The overall performance results identify applications in which our coroutine implementation model has definite strengths and weaknesses. Finally, our implementation approach is compared with modified stack implementations used in other ADT systems.
42894925
Modeling of Context-Aware Self-Adaptive Applications in Ubiquitous and Service-Oriented Environments
Mobile computing in ubiquitous environments has to cope with both predictable and unpredictable changes in the execution context, which introduces the need for context-aware adaptive applications. Such environments are also characterized by dynamically discoverable services that can be utilized by applications to improve their functionality and quality of service (QoS). Thus, application adaptation decisions not only depend on context properties, but also on service availability and QoS-properties. In this chapter we present a novel comprehensive modeling approach that facilitates the model-driven development of such applications. Our focus is on modeling concepts which align the description of services and their QoS-properties with the context modeling approach. We provide a harmonized view on context and service properties, bridging the syntactical and semantic differences through an ontology. We also consider related aspects like semantic service discovery and service level agreements.
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).
14525136
Model-based EEG and MEG Source Reconstruction Methods
Neurophysiological activity, as measured with EEG or MEG, has its origin in the brain. For most EEG and MEG experiments it is known beforehand, that the folded gray matter of the cortex is the location of the neurons whose activity is measured. These neurons, the pyramidal cells, are oriented perpendicular to the cortical surface. Triangulation of the segmented cortical surface yields a triangle net holding all anatomical information which is necessary to guide the reconstruction procedure. We have developed dedicated 3D region-growing, surface subsampling, normal estimation, and surface triangulation algorithms for generating the cortical triangle net. Especially, triangle nets encode neighborhood information of the potential source locations. Neighborhood information provides additional source constraints that model the correlation of neuronal activation which is necessary to generate detectable fields or potentials at all. In a surface-based 3D visualization environment, reconstruction results are displayed as color-coded overlays together with the cortical surface, the measurement setup, and arbitrary MR data slices. The algorithms described are part of the source reconstruction software package CURRY.