Abstract
We report work on the mapping between the speech signal and articulatory trajectories from the MOCHA database. Contrasting previous works that used Neural Networks for the same task, we employ Support Vector Regression as our main tool, and Principal Component Analysis as an auxiliary one. Our results are comparable, even though, due to training time considerations we use only a small portion of the available data.
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References
Richmond, K.: Estimating Articulatory Parameters from the Speech Signal. PhD thesis, The Center for Speech Technology Research, Edinburgh (2002)
Carreira-Perpiñán, M.A.: Continuous Latent Variable Models for Dimensionality Reduction and Sequential Data Reconstruction. PhD thesis, University of Sheffield, UK (2001)
Wrench, A.A., Hardcastle, W.J.: A multichannel articulatory database and its application for automatic speech recognition. In: 5th Seminar on Speech Production: Models and Data, Kloster Seeon, Bavaria, pp. 305–308 (2000)
Smola, A., Schölkhopf, B.: A tutorial on support vector regression. Statistics and Computing 14, 199–222 (2004)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Davis, S.B., Mermelstein, P.: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. In: Waibel, A., Lee, K.F. (eds.) Readings in speech recognition, pp. 65–74. Morgan Kaufmann Publishers Inc, San Francisco (1990)
Brooks, M. (The VOICEBOX toolkit) Software vailable, at http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html
Weston, J., Gretton, A., Elisseeff, A.: SVMpractical session (how to get good results without cheating). (Machine Learning Summer School 2003, Tuebingen, Germany.)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines, Software (2001), available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Bakir, G., Bottou, L., Weston, J.: Breaking SVM complexity with cross training. In: 18th Annual Conference on Neural Information Processing Systems, NIPS 2004 (2004)
Hyvärinen, A., Oja, E.: Independent component analysis: Algorithms and applications. Neural Networks 13, 411–430 (2000)
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© 2005 Springer-Verlag Berlin Heidelberg
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Toutios, A., Margaritis, K. (2005). Mapping the Speech Signal onto Electromagnetic Articulography Trajectories Using Support Vector Regression. In: Matoušek, V., Mautner, P., Pavelka, T. (eds) Text, Speech and Dialogue. TSD 2005. Lecture Notes in Computer Science(), vol 3658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551874_41
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DOI: https://doi.org/10.1007/11551874_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28789-6
Online ISBN: 978-3-540-31817-0
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