A neural-wavelet architecture for voice conversion

Neurocomputing

Volumn 71, Issue 1-3, 2007, Pages 174-180

  Guido, Rodrigo Capobianco ^a   Sasso Vieira, Lucimar ^a   Barbon Júnior, Sylvio ^a   Sanchez, Fabrício Lopes ^a   Dias Maciel, Carlos ^a   Silva Fonseca, Everthon ^a   Carlos Pereira, José ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

RBF neural networks; Voice conversion; Wavelet transforms

Indexed keywords

SPEECH SYNTHESIS; WAVELET TRANSFORMS;

NEURAL-WAVELET ARCHITECTURE; VOICE CONVERSION;

RADIAL BASIS FUNCTION NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATIC SPEECH RECOGNITION; CONTROLLED STUDY; HUMAN; HUMAN EXPERIMENT; NORMAL HUMAN; PRIORITY JOURNAL; RADIAL BASED FUNCTION; SPEECH ANALYSIS; THEORETICAL STUDY; VOICE CHANGE;

EID: 35649016035     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.08.010     Document Type: Article

References (18)

1. M. Abe, S. Nakamura, K. Shikano, H. Kuwabara, Voice conversion through vector quantization. in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 98), vol. 1, 1988, pp. 655-658
2. Addison P.S. The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance (2002), Institute of Physics Publishing, Edinburgh
3. Bosi M., and Goldberg R. Introduction to Digital Audio Coding and Standards. second ed. (2003), Kluwer Academic Publishers, Massachusetts
4. Deng L., and O'Shaughnessy O. Speech Processing: A Dynamic and Optimization-Oriented Approach (2003), Marcel Dekker Inc, New York, USA
5. C. Drioli, Radial basis function networks for conversion of sound speech spectra, EURASIP J. Appl. Signal Process. (1) (2001) 36-40.
6. Furui S. Research on individuality features in speech waves and automatic speaker recognition techniques. Speech Commun. 5 2 (1986) 183-197
7. R.C. Guido, et al., A study on the best wavelet for audio compression, in: 40th IEEE ASILOMAR International Conference on Signals, Systems and Computers, Pacific-Grove, CA, USA, 2006, pp. 2115-2118.
8. Haykin S. Neural Networks: A Comprehensive Foundation. second ed. (1998), Prentice-Hall, Upper Saddle River, NJ
9. Haykin S. Adaptive Filter Theory. third ed. (1999), Prentice-Hall, Upper Saddle River, NJ
10. Jensen A., and Cour-Harbo A. Ripples in Mathematics: The Discrete Wavelet Transform (2000), Springer, New York, USA
11. A. Kain, M. Macon, Spectral voice conversion for text-to-speech synthesis, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 98), vol. 1, 1998, pp. 285-288.
12. A. Mouchtaris, J. Van der Spiegel, P. Mueller, Non-parallel training for voice conversion by maximum likelihood constrained adaptation, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 04), vol. 1, 17-21, 2004, pp. I1-I4.
13. Orphanidou C., Moroz I.M., and Roberts S.J. Wavelet-based voice morphing. WSEAS J. Syst. 10 3 (2004) 3297-3302
14. Strang G., and Nguyen T. Wavelets and Filter Banks (1997), Wellesley-Cambridge Press, Wellesley, MA, USA
15. TIMIT corpus: Linguistic Data Consortium 〈http://www.ldc.upenn.edu/〉.
16. O. Turk, L.M. Arslan, Subband based voice conversion, in: Proceedings of the International Conference on Spoken Language Processing (ICSLP), vol. 1, 2002, pp. 289-293.
17. Valbret H. Voice transformation using PSOLA technique. Speech Commun. 11 2-3 (1992) 175-187
18. Wu C.H., Hsia C.C., Liu T.H., and Wang J.F. Voice conversion using duration-embedded bi-HMMs for expressive speech synthesis. IEEE Trans. Audio Speech Language Process. 14 4 (2006) 1109-1116