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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Bahl, L.R., Brown, P.F., De Souza, P.V., Mercer, R.L.: Maximum mutual information estimation of hidden Markov model parameters. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing, Tokyo, Japan, vol. 1, pp. 49–52 (1986)
Ben-Yishai, A., Burshtein, D.: A discriminative training algorithm for hidden Markov models. IEEE Transactions on Speech and Audio Processing 12(3), 204–216 (2004)
Bijak, K.: Genetic Algorithms as an Alternative Method of Parameter Estimation and Finding Most Likely Sequences of Hidden Markov Chains for HMMs and Hybrid HMM/ANN Models. Fundamenta Informaticae (2008)
Bilmes, J.: What HMMs Can’t Do. In: Beyond HMM: Workshop on Statiatical Modeling Approach for Speech Recognition, Kyoto, Japan (December 2004) (ATR Invited Paper and Lecture)
Branke, J., Deb, K., Miettinen, K.: Multiobjective Optimization: Interactive and Evolutionary Approaches. Springer, Heidelberg (2008)
Coello Coello, C.A., Pulido, G.T.: A micro-genetic algorithm for multiobjective optimization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 126–140. Springer, Heidelberg (2001)
Deb, K., Agrawal, S., Pratap, A., Mayerivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2000)
Lee, J.-S., Park, C.H.: Discriminative training of hidden Markov models by multiobjective optimization for visual speech recognition. In: Proceedings of IEEE International Joint Conference on Neural Networks, IJCNN 2005 (2005)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 2, 257–286 (1989)
Thomsen, R.: Evolving the Topology of Hidden Markov Models using Evolutionary Algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN VII 2002. LNCS, vol. 2439, pp. 861–870. Springer, Heidelberg (2002)
Wielgat, R., Zielinski, T.P., Swietojanski, P., Żoladz, P., Krol, D., Wozniak, T., Grabias, S.: Comparison of Hmm And Dtw Methods. In: Automatic Recognition of Pathological Phoneme Pronunciation, INTERSPEECH 2007, Antwerp, Belgium, August 27-31 (2007)
Won, K., Krogh, A., Prugel-Bennet, A.: Evolving the Structure of Hidden Markov Models. IEEE Transactions on Evolutionary Computations (November 2004)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In: Eurogen, Athens (2001)
Zitzler, E., Thiele, L.: An Evolutionary Algorithm for Multiobjective Optimization: The Strength Pareto Approach. Swiss Federal Institute of Technology, Technical Report 43, Zurich (1998)
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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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