Acoustic Feature Transformation Combining Average and Maximum Classification Error Minimization Criteria

Makoto SAKAI  Norihide KITAOKA  Kazuya TAKEDA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E93-D   No.7   pp.2005-2008
Publication Date: 2010/07/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E93.D.2005
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Speech and Hearing
Keyword: 
speech recognition,  dimensionality reduction,  Bayes error,  

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Summary: 
Acoustic feature transformation is widely used to reduce dimensionality and improve speech recognition performance. In this letter we focus on dimensionality reduction methods that minimize the average classification error. Unfortunately, minimization of the average classification error may cause considerable overlaps between distributions of some classes. To mitigate risks of considerable overlaps, we propose a dimensionality reduction method that minimizes the maximum classification error. We also propose two interpolated methods that can describe the average and maximum classification errors. Experimental results show that these proposed methods improve speech recognition performance.