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Volumn , Issue , 2014, Pages 343-347

Unfolded recurrent neural networks for speech recognition

Author keywords

Recurrent neural networks; Speech recognition

Indexed keywords

COMPLEX NETWORKS; MATRIX ALGEBRA; RECURRENT NEURAL NETWORKS; SPEECH COMMUNICATION;

EID: 84910072497     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (56)

References (19)
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  • 11
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    • Learning complex, extended sequences using the principle of history compression
    • J. Schmidhuber, "Learning complex, extended sequences using the principle of history compression, " Neural Computation, vol. 4, no. 2, pp. 234-242, 1992.
    • (1992) Neural Computation , vol.4 , Issue.2 , pp. 234-242
    • Schmidhuber, J.1
  • 15
    • 84858976070 scopus 로고    scopus 로고
    • Feature engineering in context-dependent deep neural networks for conversational speech transcription
    • F. Seide, G. Li, X. Chien, and D. Yu, "Feature engineering in context-dependent deep neural networks for conversational speech transcription, " in Proc. ASRU, 2011.
    • (2011) Proc. ASRU
    • Seide, F.1    Li, G.2    Chien, X.3    Yu, D.4
  • 17
    • 84893691530 scopus 로고    scopus 로고
    • Speaker adaptation of neural network acoustic models using i-vectors
    • G. Saon, H. Soltau, D. Nahamoo, and M. Picheny, "Speaker adaptation of neural network acoustic models using i-vectors, " in Proc. ASRU, 2013.
    • (2013) Proc. ASRU
    • Saon, G.1    Soltau, H.2    Nahamoo, D.3    Picheny, M.4
  • 18
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    • Low-rank matrix factorization for deep neural network training with high-dimensional output targets
    • T. Sainath, B. Kingsbury, V. Sindhwani, E. Arisoy, and B. Ramabhadran, "Low-rank matrix factorization for deep neural network training with high-dimensional output targets, " in Proc. of ICASSP, 2013.
    • (2013) Proc. of ICASSP
    • Sainath, T.1    Kingsbury, B.2    Sindhwani, V.3    Arisoy, E.4    Ramabhadran, B.5
  • 19
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    • Scalable minimum bayes risk training of deep neural network acoustic models using distributed hessian-free optimization
    • B. Kingsbury, T. 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.2    Soltau, H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.