By Werner Krauth (auth.), János Kertész, Imre Kondor (eds.)
Computer simulation has turn into a simple software in lots of branches of physics reminiscent of statistical physics, particle physics, or fabrics technology. the applying of effective algorithms is at the least as vital pretty much as good in large-scale computation. This quantity includes didactic lectures on such suggestions in keeping with actual perception. The emphasis is on Monte Carlo equipment (introduction, cluster algorithms, reweighting and multihistogram thoughts, umbrella sampling), effective information research and optimization equipment, yet features of supercomputing, the answer of stochastic differential equations, and molecular dynamics also are mentioned. The publication addresses graduate scholars and researchers in theoretical and computational physics.
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Extra resources for Advances in Computer Simulation: Lectures Held at the Eötvös Summer School in Budapest, Hungary, 16–20 July 1996
This is an example of the "sheep and goats" phenomenon. Speaker adaptation (normalisation) methods can usually be classified into supervised and unsupervised methods. Unsupervised, online, instantaneous/incremental adaptation is ideal, since the system works as if it were a speaker-independent system, and it performs increasingly better as it is used. However, since we have to adapt many phonemes using a limited amount of adaptation data including only a limited number of phonemes, it is crucial to use reasonable modelling of speaker-to-speaker variability or constraints.
Since the optical flow is computed without extracting the speaker's lip contours and location, robust visual features can be obtained for lip movements. In order to increase the robustness, a grey-scale image covering the Speech Recognition Technology in Multimodal/Ubiquitous Environments r 1 Visual ' processing \ Speech processing Tactile processing API's Fusion agent for multimodal integration Software application / / Figure 4- Architecture of a multimodal human-computer interface. mouth area is passed through a smoothing process, and random noise is added before calculating the optical flow.
How to extract contextual information, predict users' responses, and focus on keywords are very important issues. Stochastic language modelling, such as bigrams and trigrams, has been a very powerful tool, so it would be very effective to extend its utility by incorporating semantic knowledge. It would also be useful to integrate unification grammars and context-free grammars for efficient word prediction. Style shifting is also an important problem in spontaneous speech recognition. In typical laboratory experiments, speakers are reading lists of words rather than trying to accomplish a real task.