Automatic Selection of Pareto-Optimal Topologies of Hidden Markov Models Using Multicriteria Evolutionary Algorithms

Pawel Swietojanski, Robert Wielgat, Tomasz Zielinski

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationApplications of Evolutionary Computation
Subtitle of host publicationEvoApplications 2011: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Torino, Italy, April 27-29, 2011, Proceedings, Part I
EditorsCecilia Di Chio, Stefano Cagnoni, Carlos Cotta, Marc Ebner, Anikó Ekárt, Anna I. Esparcia-Alcázar, Juan J. Merelo, Ferrante Neri, Mike Preuss, Hendrik Richter, Julian Togelius, Georgios N. Yannakakis
Place of PublicationBerlin, Heidelberg
PublisherSpringer
Pages224-233
Number of pages10
ISBN (Print)978-3-642-20525-5
DOIs
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume6624
ISSN (Print)0302-9743

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