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  1. A500 情報学部/情報学研究科・情報文化学部・情報科学研究科
  2. A500a 雑誌掲載論文
  3. 学術雑誌

Linear Discriminant Analysis Using a Generalized Mean of Class Covariances and Its Application to Speech Recognition

http://hdl.handle.net/2237/14967
http://hdl.handle.net/2237/14967
1ba53acc-5c1c-4386-be5b-a037dcf1b9ca
名前 / ファイル ライセンス アクション
394.pdf 394.pdf (430.3 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2011-06-28
タイトル
タイトル Linear Discriminant Analysis Using a Generalized Mean of Class Covariances and Its Application to Speech Recognition
言語 en
著者 SAKAI, Makoto

× SAKAI, Makoto

WEKO 41138

en SAKAI, Makoto

Search repository
KITAOKA, Norihide

× KITAOKA, Norihide

WEKO 41139

en KITAOKA, Norihide

Search repository
NAKAGAWA, Seiichi

× NAKAGAWA, Seiichi

WEKO 41140

en NAKAGAWA, Seiichi

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 Copyright (C) 2008 IEICE
キーワード
主題Scheme Other
主題 speech recognition
キーワード
主題Scheme Other
主題 feature extraction
キーワード
主題Scheme Other
主題 multidimensional signal processing
抄録
内容記述 To precisely model the time dependency of features is one of the important issues for speech recognition. Segmental unit input HMM with a dimensionality reduction method has been widely used to address this issue. Linear discriminant analysis (LDA) and heteroscedastic extensions, e.g., heteroscedastic linear discriminant analysis (HLDA) or heteroscedastic discriminant analysis (HDA), are popular approaches to reduce dimensionality. However, it is difficult to find one particular criterion suitable for any kind of data set in carrying out dimensionality reduction while preserving discriminative information. In this paper, we propose a new framework which we call power linear discriminant analysis (PLDA). PLDA can be used to describe various criteria including LDA, HLDA, and HDA with one control parameter. In addition, we provide an efficient selection method using a control parameter without training HMMs nor testing recognition performance on a development data set. Experimental results show that the PLDA is more effective than conventional methods for various data sets.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 Institute of Electronics, Information and Communication Engineers
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
関連情報
関連タイプ isVersionOf
識別子タイプ URI
関連識別子 http://www.ieice.org/jpn/trans_online/index.html
ISSN
収録物識別子タイプ PISSN
収録物識別子 0916-8532
書誌情報 en : IEICE transactions on information and systems

巻 E91-D, 号 3, p. 478-487, 発行日 2008-03-01
著者版フラグ
値 publisher
URI
識別子 http://www.ieice.org/jpn/trans_online/index.html
識別子タイプ URI
URI
識別子 http://hdl.handle.net/2237/14967
識別子タイプ HDL
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