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Volumn , Issue , 2013, Pages 315-320

Improvements to deep convolutional neural networks for LVCSR

Author keywords

[No Author keywords available]

Indexed keywords

BROADCAST NEWS; CONVOLUTIONAL NEURAL NETWORK; DEEP NEURAL NETWORKS; SPEAKER ADAPTATION; SPECTRAL VARIATION; WORD ERROR RATE;

EID: 84893654379     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ASRU.2013.6707749     Document Type: Conference Paper
Times cited : (194)

References (18)
  • 3
    • 84867605836 scopus 로고    scopus 로고
    • Applying convolutional neural network concepts to hybrid NN-HMM model for speech recognition
    • O. Abdel-Hamid, A. Mohamed, H. Jiang, and G. Penn, "Applying Convolutional Neural Network Concepts to Hybrid NN-HMM Model for Speech Recognition, " in Proc. ICASSP, 2012.
    • (2012) Proc. ICASSP
    • Abdel-Hamid, O.1    Mohamed, A.2    Jiang, H.3    Penn, G.4
  • 5
    • 84890545163 scopus 로고    scopus 로고
    • A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion
    • L. Deng, O. Abdel-Hamid, and D. Yu, "A Deep Convolutional Neural Network using Heterogeneous Pooling for Trading Acoustic Invariance with Phonetic Confusion, " in Proc. ICASSP, 2013.
    • (2013) Proc. ICASSP
    • Deng, L.1    Abdel-Hamid, O.2    Yu, D.3
  • 9
    • 5044231640 scopus 로고    scopus 로고
    • Learning methods for generic object recognition with invariance to pose and lighting
    • Y. LeCun, F. Huang, and L. Bottou, "Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting, " in Proc. CVPR, 2004.
    • (2004) Proc. CVPR
    • Lecun, Y.1    Huang, F.2    Bottou, L.3
  • 10
    • 0032050110 scopus 로고    scopus 로고
    • Maximum likelihood linear transformations for HMM-based speech recognition
    • M.J.F. Gales, "Maximum likelihood linear transformations for HMMbased Speech Recognition, " Computer Speech and Language, vol. 12, no. 2, pp. 75-98, 1998. (Pubitemid 128383747)
    • (1998) Computer Speech and Language , vol.12 , Issue.2 , pp. 75-98
    • Gales, M.J.F.1
  • 11
    • 84878379108 scopus 로고    scopus 로고
    • Scalable minimum bayes risk training of deep neural network acoustic models using distributed hessian-free optimization
    • B. Kingsbury, T. N. Sainath, and H. Soltau, "Scalable Minimum Bayes Risk Training of Deep Neural Network Acoustic Models Using Distributed Hessian-free Optimization, " in Proc. Interspeech, 2012.
    • (2012) Proc. Interspeech
    • Kingsbury, B.1    Sainath, T.N.2    Soltau, H.3
  • 12
    • 84890527827 scopus 로고    scopus 로고
    • Improving deep neural networks for LVCSR using rectified linear units and dropout
    • G.E. Dahl, T.N. Sainath, and G.E. Hinton, "Improving Deep Neural Networks for LVCSR Using Rectified Linear Units and Dropout, " in Proc. ICASSP, 2013.
    • (2013) Proc. ICASSP
    • Dahl, G.E.1    Sainath, T.N.2    Hinton, G.E.3
  • 15
    • 70349213445 scopus 로고    scopus 로고
    • Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling
    • B. Kingsbury, "Lattice-Based Optimization of Sequence Classification Criteria for Neural-Network Acoustic Modeling, " in Proc. ICASSP, 2009.
    • (2009) Proc. ICASSP
    • Kingsbury, B.1
  • 18
    • 84867593213 scopus 로고    scopus 로고
    • Auto-encoder bottleneck features using deep belief networks
    • T. N. Sainath, B. Kingsbury, and B. Ramabhadran, "Auto-Encoder Bottleneck Features Using Deep Belief Networks, " in Proc. ICASSP, 2012.
    • (2012) Proc. ICASSP
    • Sainath, T.N.1    Kingsbury, B.2    Ramabhadran, B.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.