Skip to main content
Have a personal or library account? Click to login
Storytelling Voice Conversion: Evaluation Experiment Using Gaussian Mixture Models Cover

Storytelling Voice Conversion: Evaluation Experiment Using Gaussian Mixture Models

Open Access
|Sep 2015

References

  1. [1] LEE, H. J. : Fairy Tale Storytelling System: Using Both Prosody and Text for Emotional Speech Synthesis, In: Convergence and Hybrid Information Technology (Lee, G., Howard, D., Ślȩzak, D., Hong, Y.S., eds.), Communications in Computer and Information Science, vol. 310, Springer, Berlin Heidelberg, 2012, pp. 317–324.
  2. [2] ALCANTARA, J. A.—LU, L. P.—MAGNO, J. K.—SORIANO, Z.—ONG, E.—RESURRECCION, R. : Emotional Narration of Children’s Stories,, In: Theory and Practice of Computation (Nishizaki, S.Y., Numao, M., Caro, J., Suarez, M.T., eds.), Proceedings in Information and Communication Technology, vol. 5, Springer, Japan, 2012, pp. 1–14.
  3. [3] DOUKHAN, D.—ROSSET, S.—RILLIARD, A.—D’ALESSANDRO, C.—ADDA-DECKER, M. : Text and Speech Corpora for Text-to-Speech Synthesis of Tales,, In: Proceedings of the 8-th International Conference on Language Resources and Evaluation, Istanbul, Turkey, 2012, pp. 1003–1010.
  4. [4] MAENO, Y.—NOSE, T.—KOBAYASHI, T.—KORIYAMA, T.—IJIMA, Y.—NAKAJIMA, H.—MIZUNO, H.—YOSHIOKA, O. : Prosodic Variation Enhancement Using Unsupervised Context Labeling for HMM-based Expressive Speech Synthesis, Speech Communication(2014), 144–154.
  5. [5] PŘIBIL, J.—PŘIBILOVÁ, A. : Czech TTS Engine for Braille Pen Device Based on Pocket PC Platform, Proc. of the 16th Conference Electronic Speech Signal Processing ESSP 05 joined with the 15th Czech-GermanWorkshop Speech Processing (Vch, R., ed.), 2005, pp. 402–408.
  6. [6] PŘIBILOVÁ, A.—PŘIBIL, J. : Spectrum Modification for Emotional Speech Synthesis, In: Multimodal Signals: Cognitive and Algorithmic Issues (Esposito, A., Hussain, A., Marinaro, M., Martone, R., eds.), LNAI 5398, Springer-Verlag Berlin Heidelberg, 2009, pp. 232–241.
  7. [7] PŘIBIL, J.—PŘIBILOVÁ, A. : Application of Expressive Speech in TTS System with Cepstral Description., In: Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction (Esposito, A., Bourbakis, N., Avouris, N., Hatrzilygeroudis, I., eds.), LNAI 5042, Springer-Verlag, Berlin Heidelberg, 2008, pp. 201–213.
  8. [8] BLAUERT, J.—JEKOSCH, U. : A Layer Model of Sound Quality, Journal of the Audio Engineering Society(2012), 4–12.
  9. [9] LEGÁT, M.—MATOUŠEK, J. : Design of the Test Stimuli for the Evaluation of Concatenation Cost Functions, In: Text, Speech and Dialogue 2009 (MATOUŠEK, V., eds.), LNCS 5729, Springer, Heidelberg, 2009, pp. 339–346.
  10. [10] BELLO, C.—RIBAS, D.—CALVO, J. R.—FERRER, C. A. : From Speech Quality Measures to Speaker Recognition Performance., In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (Bayro-Corrochano, E., Hancock, E., eds.), LNCS 8827, Springer International Publishing Switzerland, 2014, pp. 199–206.
  11. [11] ROMPORT, J.—MATOUŠEK, J. : Formal Prosodic Structures and Their Application in NLP, In: Text, Speech and Dialogue 2005 (Matouek, V.., eds.), LNCS 3658, Springer-Verlag, Berlin Heidelberg, 2005, pp. 371–378.
  12. [12] JEONG, Y. : Joint Speaker and Environment Adaptation Using TensorVoice for Robust Speech Recognition, Speech Communication(2014), 1–10.
  13. [13] REYNOLDS, D. A.—ROSE, R. C. : Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models, IEEE Transactions on Speech and Audio Processing(1995), 72–83.
  14. [14] MUHAMMAD, G.—ALGHATHBAR, K. : Environment Recognition for Digital Audio Forensics Using MPEG-7 and Mel Cepstral Features, Journal of Electrical EngineeringNo. 4 (2011), 199–205.
  15. [15] PISHRAVIAN, A.—SAHAF, M. R. A. : Application of Independent Component Analysis for Speech-Music Separation Using An Efficient Score Function Estimation, Journal of Electrical EngineeringNo. 6 (2012), 380–385.
  16. [16] PŘIBIL, J.—PŘIBILOVÁ, A. : Emotional Style Conversion in the TTS System with Cepstral Description, In: Verbal and Nonverbal Communication Behaviours (Esposito, A., Faundez-Zanuy, M., Keller, E., Marinaro, M., eds.), LNAI 4775, Springer-Verlag, Berlin Heidelberg New York, 2007, pp. 65–73.
  17. [17] VCH, R.—PŘIBIL, J.—SMÉKAL, Z. : New Cepstral Zero-Pole Vocal Tract Models for TTS Synthesis, Proc. of IEEE Region 8 EUROCON’2001, vol. 2, 2001, pp. 458–462.
  18. [18] MADHU, N. : Note on Measures for Spectral Flatness, Electronics LettersNo. 23 (2009), 1195–1196.
  19. [19] SHAH, N. H. : Numerical Methods with C++ Programming, Prentice-Hall Of India Learning Private Limited, New Delhi, 2009.
  20. [20] HOSSEINZADEH, D.—KRISHNAN, S. : On the Use of Complementary Spectral Features for Speaker Recognition, EURASIP Journal on Advances in Signal Processing (2008), Article ID 258184.
  21. [21] SOUSA. R.—FERREIRA, A.—ALKU, P. : The Harmonic and Noise Information of the Glottal Pulses, Speech, Biomedical Signal Processing and Control(2014), 137–143.
  22. [22] LECLERC, I.—DAJANI, H. R.—GIGUERE, C. : Differences in Shimmer Across Formant Regions, Journal of VoiceNo. 6 (2013), 685–690.
  23. [23] PŘIBIL, J.—PŘIBILOVÁ, A.—ĎURAČKOVÁ, D. : Evaluation of Spectral and Prosodic Features of Speech Affected by Orthodontic Appliances using the GMM Classifier, Journal of Electrical Engineering(2014), 30–36.
  24. [24] PŘIBIL, J.—PŘIBILOVÁ, A. : Determination of Formant Features in Czech and Slovak for GMM Emotional Speech Classifier, Radioengineering(2013), 52–59.
  25. [25] NABNEY, I. T. : Netlab Pattern Analysis Toolbox, (c)1996 - 2001. Retrieved 16 February 2012 from.
  26. [26] PŘIBIL, J.—PŘIBILOVÁ, A.—MATOUŠEK, J. : Experiment with Evaluation of Quality of the Synthetic Speech by the GMM Classifier, In: Text, Speech and Dialogue, Proc. of the 16th International Conference TSD 2013, Plzen, Czech Republic September 2013 (Habernal, I., Matoušek, V., eds.), LNAI 8082, Springer-Verlag, Berlin Heidelberg, 2013, pp. 241–248.
  27. [27] DILEEP, A. D.—SEKHAR, C. CH. : Class-Specific GMM Based Intermediate Matching Kernel for Classification of Varying Length Patterns of Long Duration Speech Using Support Vector Machines, Speech Communication(2014), 126–143.
  28. [28] ZHAO, J.—JIANG, Q. : Probabilistic PCA for t-Distributions, NeurocomputingNo. 16-18 (2006), 2217–2226.
DOI: https://doi.org/10.2478/jee-2015-0032 | Journal eISSN: 1339-309X (formerly 1335-3632) | Journal ISSN: 1335-3632
Language: English
Page range: 194 - 202
Submitted on: Dec 1, 2014
Published on: Sep 19, 2015
Published by: Slovak University of Technology in Bratislava
In partnership with: Paradigm Publishing Services

© 2015 Jiří Přibil, Anna Přibilová, Daniela Ďuračková, published by Slovak University of Technology in Bratislava
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.