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Volumn , Issue , 2012, Pages 4349-4352

Latent variable speaker adaptation of Gaussian mixture weights and means

Author keywords

eigenvoice and weight adaptation; fast speaker adaptation; latent variable method; non negative matrix factorization; speaker adaptive training

Indexed keywords

ADAPTIVE TRAINING; EIGENVOICES; FAST SPEAKER ADAPTATION; LATENT VARIABLE METHODS; NONNEGATIVE MATRIX FACTORIZATION;

EID: 84867600898     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2012.6288882     Document Type: Conference Paper
Times cited : (5)

References (10)
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  • 2
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  • 3
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    • Maximum a posteriori adaptation for large scale HMM recognizers
    • G. Zavaliagkos and R. Schwartz, "Maximum a posteriori adaptation for large scale HMM recognizers," in Proc. ICASSP, 1996, vol. 2, pp. 725-728.
    • Proc. ICASSP, 1996 , vol.2 , pp. 725-728
    • Zavaliagkos, G.1    Schwartz, R.2
  • 4
    • 0032050110 scopus 로고    scopus 로고
    • Maximum likelihood linear transformations for HMM-based speech recognition
    • M. J. F. Gales, "Maximum likelihood linear transformations for HMM-based speech recognition," Comp. Speech and Lang., vol. 12, pp. 75-98, 1998.
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  • 5
    • 80051649263 scopus 로고    scopus 로고
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    • Povey, D.1    Yao, K.2
  • 7
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    • Rapid speaker adaptation with speaker adaptive training and non-negative matrix factorization
    • X. Zhang, K. Demuynck, and H. Van hamme, "Rapid speaker adaptation with speaker adaptive training and non-negative matrix factorization," in Proc. ICASSP, May 2011, pp. 4456-4459.
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    • Zhang, X.1    Demuynck, K.2    Van Hamme, H.3
  • 10
    • 85135280100 scopus 로고    scopus 로고
    • Maximum likelihood eigenspace and MLLR for speech recognition in noisy environments
    • P. Nguyen, C. Wellekens, and J.-C. Junqua, "Maximum likelihood eigenspace and MLLR for speech recognition in noisy environments," in Eurospeech, 1999, pp. 2519-2522.
    • (1999) Eurospeech , pp. 2519-2522
    • Nguyen, P.1    Wellekens, C.2    Junqua, J.-C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.