Exploring the use of speech features and their corresponding distribution characteristics for robust speech recognition

Shih Hsiang Lin, Berlin Chen, Yao Ming Yeh

研究成果: 雜誌貢獻文章同行評審

18 引文 斯高帕斯(Scopus)

摘要

The performance of current automatic speech recognition (ASR) systems often deteriorates radically when the input speech is corrupted by various kinds of noise sources. Several methods have been proposed to improve ASR robustness over the last few decades. The related literature can be generally classified into two categories according to whether the methods are directly based on the feature domain or consider some specific statistical feature characteristics. In this paper, we present a polynomial regression approach that has the merit of directly characterizing the relationship between speech features and their corresponding distribution characteristics to compensate for noise interference. The proposed approach and a variant were thoroughly investigated and compared with a few existing noise robustness approaches. All experiments were conducted using the Aurora-2 database and task. The results show that our approaches achieve considerable word error rate reductions over the baseline system and are comparable to most of the conventional robustness approaches discussed in this paper.

原文英語
文章編號4740142
頁(從 - 到)84-94
頁數11
期刊IEEE Transactions on Audio, Speech and Language Processing
17
發行號1
DOIs
出版狀態已發佈 - 2009 一月

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

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