Improved linear discriminant analysis considering empirical pairwise classification error rates

Hung Shin Lee, Berlin Chen

研究成果: 書貢獻/報告類型會議論文篇章

2 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題Proceedings - 2008 6th International Symposium on Chinese Spoken Language Processing, ISCSLP 2008
頁面149-152
頁數4
DOIs
出版狀態已發佈 - 2008
事件2008 6th International Symposium on Chinese Spoken Language Processing, ISCSLP 2008 - Kunming, 中国
持續時間: 2008 十二月 162008 十二月 19

出版系列

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

其他

其他2008 6th International Symposium on Chinese Spoken Language Processing, ISCSLP 2008
國家/地區中国
城市Kunming
期間2008/12/162008/12/19

ASJC Scopus subject areas

  • 電腦科學應用
  • 資訊系統
  • 軟體

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