Generalized likelihood ratio discriminant analysis

Hung Shin Lee, Berlin Chen

研究成果: 書貢獻/報告類型會議貢獻

5 引文 斯高帕斯(Scopus)

摘要

In the past several decades, classifier-independent front-end feature extraction, where the derivation of acoustic features is lightly associated with the back-end model training or classification, has been prominently used in various pattern recognition tasks, including automatic speech recognition (ASR). In this paper, we present a novel discriminative feature transformation, named generalized likelihood ratio discriminant analysis (GLRDA), on the basis of the likelihood ratio test (LRT). It attempts to seek a lower dimensional feature subspace by making the most confusing situation, described by the null hypothesis, as unlikely to happen as possible without the homoscedastic assumption on class distributions. We also show that the classical linear discriminant analysis (LDA) and its well-known extension - heteroscedastic linear discriminant analysis (HLDA) can be regarded as two special cases of our proposed method. The empirical class confusion information can be further incorporated into GLRDA for better recognition performance. Experimental results demonstrate that GLRDA and its variant can yield moderate performance improvements over HLDA and LDA for the large vocabulary continuous speech recognition (LVCSR) task.

原文英語
主出版物標題Proceedings of the 2009 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2009
頁面158-163
頁數6
DOIs
出版狀態已發佈 - 2009 十二月 1
事件2009 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2009 - Merano, 意大利
持續時間: 2009 十二月 132009 十二月 17

出版系列

名字Proceedings of the 2009 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2009

其他

其他2009 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2009
國家意大利
城市Merano
期間09/12/1309/12/17

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Signal Processing

指紋 深入研究「Generalized likelihood ratio discriminant analysis」主題。共同形成了獨特的指紋。

  • 引用此

    Lee, H. S., & Chen, B. (2009). Generalized likelihood ratio discriminant analysis. 於 Proceedings of the 2009 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2009 (頁 158-163). [5373392] (Proceedings of the 2009 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2009). https://doi.org/10.1109/ASRU.2009.5373392