A new feature normalization scheme based on eigenspace for noisy speech recognition

Yoonjae Lee, Hanseok Ko

Research output: Contribution to journalArticle


We propose a new feature normalization scheme based on eigenspace, for achieving robust speech recognition. In particular, we employ the Mean and Variance Normalization (MVN) in eigenspace using unique and independent eigenspaces to cepstra, delta and delta-delta cepstra respectively. We also normalize training data in eigenspace and get the model from the normalized training data. In addition, a feature space rotation procedure is introduced to reduce the mismatch of training and test data distribution in noisy condition. As a result, we obtain a substantial recognition improvement over the basic eigenspace normalization.

Original languageEnglish
Pages (from-to)76-78
Number of pages3
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2004 Dec 1


ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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