A note on the robust stability of uncertain stochastic fuzzy systems with time-delays

Copyright [2004] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or 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 must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.Takagi-Sugeno (T-S) fuzzy models are now often used to describe complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear submodels. In this note, the T-S fuzzy model approach is exploited to establish stability criteria for a class of nonlinear stochastic systems with time delay. Sufficient conditions are derived in the format of linear matrix inequalities (LMIs), such that for all admissible parameter uncertainties, the overall fuzzy system is stochastically exponentially stable in the mean square, independent of the time delay. Therefore, with the numerically attractive Matlab LMI toolbox, the robust stability of the uncertain stochastic fuzzy systems with time delays can be easily checked

The Role of Multiple Articulatory Channels of Sign-Supported Speech Revealed by Visual Processing

Purpose The use of sign-supported speech (SSS) in the education of deaf students has been recently discussed in relation to its usefulness with deaf children using cochlear implants. To clarify the benefits of SSS for comprehension, 2 eye-tracking experiments aimed to detect the extent to which signs are actively processed in this mode of communication. Method Participants were 36 deaf adolescents, including cochlear implant users and native deaf signers. Experiment 1 attempted to shift observers' foveal attention to the linguistic source in SSS from which most information is extracted, lip movements or signs, by magnifying the face area, thus modifying lip movements perceptual accessibility (magnified condition), and by constraining the visual field to either the face or the sign through a moving window paradigm (gaze contingent condition). Experiment 2 aimed to explore the reliance on signs in SSS by occasionally producing a mismatch between sign and speech. Participants were required to concentrate upon the orally transmitted message. Results In Experiment 1, analyses revealed a greater number of fixations toward the signs and a reduction in accuracy in the gaze contingent condition across all participants. Fixations toward signs were also increased in the magnified condition. In Experiment 2, results indicated less accuracy in the mismatching condition across all participants. Participants looked more at the sign when it was inconsistent with speech. Conclusions All participants, even those with residual hearing, rely on signs when attending SSS, either peripherally or through overt attention, depending on the perceptual conditions.Unión Europea, Grant Agreement 31674

A motion-based approach for audio-visual automatic speech recognition

The research work presented in this thesis introduces novel approaches for both visual region of interest extraction and visual feature extraction for use in audio-visual automatic speech recognition. In particular, the speaker‘s movement that occurs during speech is used to isolate the mouth region in video sequences and motionbased features obtained from this region are used to provide new visual features for audio-visual automatic speech recognition. The mouth region extraction approach proposed in this work is shown to give superior performance compared with existing colour-based lip segmentation methods. The new features are obtained from three separate representations of motion in the region of interest, namely the difference in luminance between successive images, block matching based motion vectors and optical flow. The new visual features are found to improve visual-only and audiovisual speech recognition performance when compared with the commonly-used appearance feature-based methods. In addition, a novel approach is proposed for visual feature extraction from either the discrete cosine transform or discrete wavelet transform representations of the mouth region of the speaker. In this work, the image transform is explored from a new viewpoint of data discrimination; in contrast to the more conventional data preservation viewpoint. The main findings of this work are that audio-visual automatic speech recognition systems using the new features extracted from the frequency bands selected according to their discriminatory abilities generally outperform those using features designed for data preservation. To establish the noise robustness of the new features proposed in this work, their performance has been studied in presence of a range of different types of noise and at various signal-to-noise ratios. In these experiments, the audio-visual automatic speech recognition systems based on the new approaches were found to give superior performance both to audio-visual systems using appearance based features and to audio-only speech recognition systems

Audio-Visual Speech Recognition using Red Exclusion an Neural Networks

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Automatic Visual Speech Recognition

Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc