This paper describes a multipopulation real-coded genetic approach for recovering vocal tract area functions from speech data. The kind of data analyzed is a subset of Spanish speech signals, concretely vowels from Ve...
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This paper describes a multipopulation real-coded genetic approach for recovering vocal tract area functions from speech data. The kind of data analyzed is a subset of Spanish speech signals, concretely vowels from Venezuelan SpeechDat database of utterances, increasing novelty of the study. The method evolves parametric representations of speech articulators, with the goal set to minimizing acoustic distance respect to target, natural SpeechDat utterances. This distance is based on signal's formants and a measure of continuity of the area function. Subsequently, best learned functions are provided as input to an articulatory speech synthesizer, in order to generate artificial utterances, potentially and acoustically similar to the natural signals. Objective and subjective tests on these artificial signals have positively verified effectiveness of the genetic approach. (c) 2006 Elsevier B. V. All rights reserved.
This paper describes the use of parallel multipopulation genetic algorithms (GAs) to meet the dynamic nature of job-shop scheduling. A modified genetic technique is adopted by using a specially formulated genetic oper...
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This paper describes the use of parallel multipopulation genetic algorithms (GAs) to meet the dynamic nature of job-shop scheduling. A modified genetic technique is adopted by using a specially formulated genetic operator to provide an efficient optimisation search. The proposed technique has been successfully implemented using the programming language MATrix LABoratory (MATLAB), providing a powerful tool for job-shop scheduling. Comparisons indicate that the proposed genetic algorithm has successfully improved upon the solution obtained from conventional approaches, particularly in coping with jobshop scheduling.
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