Abstract
In this paper a novel approach of automatic selection of Hidden Markov Models (HMM) structures under Pareto-optimality criteria is presented. Proof of concept is delivered in automatic speech recognition (ASR) discipline where two research scenarios including recognition of speech disorders as well as classification of bird species using their voice are performed. The conducted research unveiled that the Pareto Optimal Hidden Markov Models (POHMM) topologies outperformed both manual structures selection based on theoretical prejudices as well as the automatic approaches that used a single objective only.
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Swietojanski, P., Wielgat, R., Zielinski, T. (2011). Automatic Selection of Pareto-Optimal Topologies of Hidden Markov Models Using Multicriteria Evolutionary Algorithms. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_23
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DOI: https://doi.org/10.1007/978-3-642-20525-5_23
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