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Bayesian Analysis of Phoneme Confusion Matrices
KTH, School of Electrical Engineering (EES). ORCA Europe Widex, Sweden.
2016 (English)In: IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, ISSN 2329-9290, Vol. 24, no 3Article in journal (Refereed) PublishedText
Abstract [en]

This paper presents a parametric Bayesian approach to the statistical analysis of phoneme confusion matrices measured for groups of individual listeners in one or more test conditions. Two different bias problems in conventional estimation of mutual information are analyzed and explained theoretically. Evaluations with synthetic datasets indicate that the proposed Bayesian method can give satisfactory estimates of mutual information and response probabilities, even for phoneme confusion tests using a very small number of test items for each phoneme category. The proposed method can reveal overall differences in performance between two test conditions with better power than conventional Wilcoxon significance tests or conventional confidence intervals. The method can also identify sets of confusion-matrix cells that are credibly different between two test conditions, with better power than a similar approximate frequentist method.

Place, publisher, year, edition, pages
IEEE , 2016. Vol. 24, no 3
Keyword [en]
Bayes methods, mutual information, parameter estimation, speech recognition
National Category
Fluid Mechanics and Acoustics Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-185071DOI: 10.1109/TASLP.2015.2512039ISI: 000372025000004ScopusID: 2-s2.0-84962860045OAI: oai:DiVA.org:kth-185071DiVA: diva2:919745
Note

QC 20160414

Available from: 2016-04-14 Created: 2016-04-11 Last updated: 2016-04-14Bibliographically approved

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Leijon, Arne
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ReferencesLink to record
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