Improved linear discriminant analysis considering empirical pairwise classification error rates

Hung Shin Lee, Berlin Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Linear discriminant analysis (LDA) is designed to seek a linear transformation that projects a data set into a lower-dimensional feature space for maximum class geometrical separability. LDA cannot always guarantee better classification accuracy, since its formulation is not in light of the properties of the classifiers, such as the automatic speech recognizer (ASR). In this paper, the relationship between the empirical classification error rates and the Mahalanobis distances of the respective class pairs of speech features is investigated, and based on this, a novel reformulation of the LDA criterion, distance-error coupled LDA (DE-LDA), is proposed. One notable characteristic of DE-LDA is that it can modulate the contribution on the between-class scatter from each class pair through the use of an empirical error function, while preserving the lightweight solvability of LDA. Experiment results seem to demonstrate that DE-LDA yields moderate improvements over LDA on the LVCSR task.

Original languageEnglish
Title of host publicationProceedings - 2008 6th International Symposium on Chinese Spoken Language Processing, ISCSLP 2008
Pages149-152
Number of pages4
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 6th International Symposium on Chinese Spoken Language Processing, ISCSLP 2008 - Kunming, China
Duration: 2008 Dec 162008 Dec 19

Publication series

NameProceedings - 2008 6th International Symposium on Chinese Spoken Language Processing, ISCSLP 2008

Other

Other2008 6th International Symposium on Chinese Spoken Language Processing, ISCSLP 2008
CountryChina
CityKunming
Period08/12/1608/12/19

Keywords

  • Empirical error function
  • Feature extraction
  • Linear discriminant analysis
  • Speech recognition

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Software

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