Abstract
In silent speech interfaces a mapping is established between biosignals captured by sensors and acoustic characteristics of speech. Recent works have shown the feasibility of a silent interface based on permanent magnet-articulography (PMA). This paper studies the performance of four different mapping methods based on Gaussian mixture models (GMMs), typical from the voice conversion field, when applied to PMA-to-spectrum conversion. The results show the superiority of methods based on maximum likelihood parameter generation (MLPG), especially when the parameters of the mapping function are trained by minimizing the generation error. Informal listening tests reveal that the resulting speech is moderately intelligible for the database under study.
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References
Qi, Y., Weinberg, B., Bi, N.: Enhancement of female esophageal and tracheoesophageal speech. J. Acoust. Soc. Am. 98, 2461–2465 (1995)
Matsui, K., Hara, N.: Enhancement of esophageal speech using formant synthesis. In: Proceedings of the ICASSP, pp. 81–84 (1999)
del Pozo, A., Young, S.J.: Continuous tracheoesophageal speech repair. In: Proceedings of the EUSIPCO, pp. 1–5 (2006)
Türkmen, H.I., Karsligil, M.E.: Reconstruction of dysphonic speech by MELP. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 767–774. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85920-8_93
Mantilla-Caeiros, A., Nakano-Miyatake, M., Perez-Meana, H.: A pattern recognition based esophageal speech enhancement system. J. Appl. Res. Tech. 8(1), 56–71 (2010)
Doi, H., Nakamura, K., Toda, T., Saruwatari, H., Shikano, K.: Esophageal speech enhancement based on statistical voice conversion with Gaussian mixture models. IEICE Trans. Inf. Syst. E93–D(9), 2472–2482 (2010)
Nakamura, K., Toda, T., Saruwatari, H., Shikano, K.: Speaking-aid systems using GMM-based voice conversion for electrolaryngeal speech. Speech Commun. 54(1), 134–146 (2012)
Doi, H., Toda, T., Nakamura, K., Saruwatari, H., Shikano, K.: Alaryngeal speech enhancement based on one-to-many eigenvoice conversion. IEEE/ACM Trans. Audio Speech Lang. Process. 22(1), 172–183 (2014)
Kello, C.T., Plaut, D.C.: A neural network model of the articulatoryacoustic forward mapping trained on recordings of articulatory parameters. J. Acoust. Soc. Am. 116(4), 2354–2364 (2004)
Toda, T., Black, A.W., Tokuda, K.: Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model. Speech Commun. 50(3), 215–227 (2008)
Denby, B., Schultz, T., Honda, K., Hueber, T., Gilbert, J.M., Brumberg, J.S.: Silent speech interfaces. Speech Commun. 52(4), 270–287 (2010)
Hofe, R., Ell, S.R., Fagan, M.J., Gilbert, J.M., Green, P.D., Moore, R.K., Rybchenko, S.I.: Speech synthesis parameter generation for the assistive silent speech interface MVOCA. In: Proceedings of the INTERSPEECH, pp. 3009–3012 (2011)
Cheah, L.A., Bai, J., Gonzalez, J.A., Ell, S.R., Gilbert, J.M., Moore, R.K., Green, P.D.: A user-centric design of permanent magnetic articulography based assistive speech technology. In: Proceedings of the BioSignals, pp. 109–116 (2015)
Gonzalez, J.A., Cheah, L.A., Gilbert, J.M., Bai, J., Ell, S.R., Green, P.D., Moore, R.K.: A silent speech system based on permanent magnet articulography and direct synthesis. Comput. Speech Lang. 39, 67–87 (2016)
Kain, A., Macon, M.W.: Spectral voice conversion for text-to-speech synthesis. In: Proceedings of the ICASSP, pp. 285–288 (1998)
Stylianou, Y., Cappé, O., Moulines, E.: Continuous probabilistic transform for voice conversion. IEEE Trans. Speech Audio Process. 6(2), 131–142 (1998)
Ye, H., Young, S.J.: Quality-enhanced voice morphing using maximum likelihood transformations. IEEE Trans. Audio Speech Lang. Process. 14(4), 1301–1312 (2006)
Toda, T., Black, A., Tokuda, K.: Voice conversion based on maximum-likelihood estimation of spectral parameter trajectory. IEEE Trans. Audio Speech Lang. Process. 15(8), 2222–2235 (2007)
Erro, D., Alonso, A., Serrano, L., Tavarez, D., Odriozola, I., Sarasola, X., Del-Blanco, E., Sanchez, J., Saratxaga, I., Navas, E., Hernaez, I.: ML parameter generation with a reformulated MGE training criterion participation in the voice conversion challenge 2016. In: Proceedings of the INTERSPEECH (2016)
Kominek, J., Black, A.W.: The CMU arctic speech databases. In: Proceedings of the 5th ISCA Speech Synthesis Workshop, pp. 223–224 (2004)
Erro, D., Sainz, I., Navas, E., Hernáez, I.: Harmonics plus noise model based vocoder for statistical parametric speech synthesis. IEEE J. Sel. Top. Sig. Process. 8(2), 184–194 (2014)
Tokuda, K., Masuko, T., Miyazaki, N., Kobayashi, T.: Multi-space probability distribution HMM. IEICE Trans. Inf. Syst. E85–D(3), 455–464 (2002)
Acknowledgements
This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (RESTORE project, TEC2015-67163-C2-1-R MINECO/FEDER, UE) and the Basque Government (ELKAROLA, KK-2015/00098). We would like to thank the Univeristy of Hull and the University of Sheffield, especially Dr. Jose A. Gonzalez, for the permission to use the PMA data in this work.
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Erro, D., Hernaez, I., Serrano, L., Saratxaga, I., Navas, E. (2016). Objective Comparison of Four GMM-Based Methods for PMA-to-Speech Conversion. In: Abad, A., et al. Advances in Speech and Language Technologies for Iberian Languages. IberSPEECH 2016. Lecture Notes in Computer Science(), vol 10077. Springer, Cham. https://doi.org/10.1007/978-3-319-49169-1_3
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