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Volumn , Issue , 2013, Pages 350-355

Discriminative piecewise linear transformation based on deep learning for noise robust automatic speech recognition

Author keywords

Automatic speech recognition; Deep learning; feature enhancement; Noise robustness

Indexed keywords

AUTOMATIC SPEECH RECOGNITION; DEEP LEARNING; FEATURE ENHANCEMENT; NOISE ROBUSTNESS; NOISE-ROBUST AUTOMATIC SPEECH RECOGNITION; NON-LINEAR RELATIONSHIPS; PIECEWISE-LINEAR TRANSFORMATION; PROBABILISTIC DISTRIBUTION;

EID: 84893709342     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ASRU.2013.6707755     Document Type: Conference Paper
Times cited : (6)

References (14)
  • 5
  • 9
    • 84893711980 scopus 로고    scopus 로고
    • Feature enhancement with joint use of consecutive corrupted and noise feature vectors with discriminative region weighting
    • (to appear
    • Suzuki Masayuki, Yoshioka Takuya, Watanabe Shinji, Minematsu Nobuaki, and Hirose Keikichi "Feature Enhancement With Joint Use of Consecutive Corrupted and Noise Feature Vectors With Discriminative Region Weighting, " IEEE TASLP, (to appear).
    • IEEE TASLP
    • Masayuki, S.1    Takuya, Y.2    Shinji, W.3    Nobuaki, M.4    Keikichi, H.5
  • 10
    • 84893640939 scopus 로고    scopus 로고
    • http://www.elda.fr/proj/aurora1.html, http://www.elda.fr/proj/aurora2. html
    • http://eurospeech2001.org/ese/NoiseRobust/index.html, http://www.elda.fr/proj/aurora1.html, http://www.elda.fr/proj/aurora2.html.
  • 11
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • DOI 10.1162/neco.2006.18.7.1527
    • Hinton, Geoffrey E and Osindero, Simon and Teh, Yee- Whye "A fast learning algorithm for deep belief nets, " Neural computation, vol. 18, no. 7, pp. 1527-1554, 2006. (Pubitemid 44024729)
    • (2006) Neural Computation , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.-W.3
  • 12
  • 13
    • 84893680316 scopus 로고    scopus 로고
    • http://htk.eng.cam.ac.uk/.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.