An artificial Neural Network (ANN) is a well known universal approximator to model smooth and continuous functions (Brown & Harris, 1994). As ANNs can realize nonlinear models, they are flexible in modeling a wide variety of real-world complex applications, such as handwriting recognition, speech recognition, fault detection, medical inspection (Zhang, 2000), etc. ANNs being applied for pattern...

The bimodal acoustic-visual nature of speech establishes sound correlations between its audio component and the corresponding articulatory information associated to the time-varying geometry of the vocal tract. In this paper we propose an estimation structure consisting of a simpliied Time-Delay Neural Network (TDNN) working on 4-5 dimensional cepstrum trajectories provided by a preceding clust...

This thesis presents work in three main directions of the automatic speech recognition field. The work within two of these – dynamic decoding and hybrid HMM/ANN speech recognition – has resulted in a real-time speech recognition system, currently in use in the human/machine dialogue demonstration system WAXHOLM, developed at the department. The third direction is fast unsupervised speaker adapt...

recognize 10 isolated letters and used artificial markers on the lips. No visual feature extraction was integrated into their model. Also of interest are some psychological studies about human speechreading and their approach to describe the human performance. This measurements could also be applied to the performance analysis of automated speechreading systems. Dodd and Campbell [3], and Demor...

In literature, we could categorise two broad main approaches for pattern recognition systems. The first category consists of linear combinatorial-type filters (LCFs) (Stamos, 2001) where commonly image analysis is done in the frequency domain with the help of Fourier Transformation (FT) (Lynn & Fuerst, 1998; Proakis & Manolakis, 1998). The second category consists of pure neural modelling metho...

In recent years video based analysis of human motion gained increased interest, which for a large part is due to the ongoing rapid developments of computer and camera hardware, such as increased CPU power, fast and modular interfaces and high quality image digitisation. A similar important role plays the development of powerful approaches for the analysis of visual data from video sources. In c...

We study model feed forward networks as time series predictors in the stationary limit The focus is on complex yet non chaotic behavior The main question we address is whether the asymptotic behavior is governed by the architecture regardless the details of the weights We nd hierarchies among classes of architectures with respect to the attractor dimension of the long term sequence they are cap...

For deep space missions, a major concern is the occurrence of large solar particle events (SPE) which can deliver doses to critical body organs in excess of 10 Gy at dose rates exceeding 1 Gy h over a period of several hours. Accurately predicting the likelihood of occurrence of these events before they begin has been an ongoing problem throughout the history of manned spaceflight. Recently, ef...

We have recently proposed neural timing networks that operate on temporal fine structure of inputs to build up and separate periodic signals with different fundamental periods (Neural Networks, 14: 737-753, 2001). Simple recurrent nets consist of arrays of coincidence detectors fed by common input lines and conduction delay loops of different recurrence times. Short-term facilitation amplifies ...

Many research efforts on travel time prediction focus predominantly on freeways, while limited work has been done on urban arterials. Among the latter, data driven models, particularly neural networks, have demonstrated promising performance. In most cases, the inputs from spatially separated sources (volumes/speeds collected by loop detectors from different locations) are combined in one singl...