Bayesian Analysis of Phoneme Confusion Matrices

Arne Leijon, Gustav Eje Henter, Martin Dahlquist

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)469-482
Number of pages14
JournalIEEE/ACM Transactions on Audio, Speech and Language Processing
Volume24
Issue number3
Early online date23 Dec 2015
DOIs
Publication statusPublished - 23 Dec 2015

Keywords / Materials (for Non-textual outputs)

  • Speech recognition
  • parameter estimation
  • mutual information
  • Bayes methods

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