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Volumn 2015-August, Issue , 2015, Pages 4849-4853

Spectral conversion using deep neural networks trained with multi-source speakers

Author keywords

deep neural networks; source speaker independent mapping; voice conversion

Indexed keywords

AUDIO SIGNAL PROCESSING; SPEECH COMMUNICATION; SPEECH PROCESSING;

EID: 84946076200     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2015.7178892     Document Type: Conference Paper
Times cited : (9)

References (16)
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  • 5
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    • Iwahashi, N.1    Sagisaka, Y.2
  • 7
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    • Parametric voice conver-sion based on bilinear frequency warping plus amplitude scal-ing
    • D. Erro, E. Navas, and I. Hernaez, Parametric voice conver-sion based on bilinear frequency warping plus amplitude scal-ing, IEEE Trans. Audio, Speech, and Lang. Process, vol. 21, no. 3, pp. 556C566, 2013
    • (2013) IEEE Trans. Audio, Speech, and Lang. Process , vol.21 , Issue.3 , pp. 556-566
    • Erro, D.1    Navas, E.2    Hernaez, I.3
  • 8
    • 57749193836 scopus 로고    scopus 로고
    • Voice conversion based on maximum-likelihood estimation of spectral parameter tra-jectory
    • nov
    • T. Toda, A.W. Black, and K. Tokuda, Voice conversion based on maximum-likelihood estimation of spectral parameter tra-jectory, IEEE Trans. Audio, Speech, and Lang. Process, vol. 15, no. 8, pp. 2222-2235, nov. 2007
    • (2007) IEEE Trans. Audio, Speech, and Lang. Process , vol.15 , Issue.8 , pp. 2222-2235
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  • 9
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    • Joint spectral distribution modeling using restricted Boltzmann machines for voice conversion
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  • 13
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    • Voice conversion in high-order eigen space us-ing deep belief nets
    • Toru Nakashika, Ryoichi Takashima, Tetsuya Takiguchi, and Yasuo Ariki, Voice conversion in high-order eigen space us-ing deep belief nets, in INTERSPEECH'13, 2013, pp. 369-372
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.