Abstract
More than ten years ago the first successful application of a nonlinear oscillator model to high-quality speech signal processing was reported (Kubin and Kleijn, 1994). Since then, numerous developments have been initiated to turn nonlinear oscillators into a standard tool for speech technology. The present contribution will review and compare several of these attempts with a special emphasis on adaptive model identification from data and the approaches to the associated machine learning problems. This includes Bayesian methods for the regularization of the parameter estimation problem (including the pruning of irrelevant parameters) and Ansatz library (Lainscsek et al., 2001) based methods (structure selection of the model). We conclude with the observation that these advanced identification methods need to be combined with a thorough background from speech science to succeed in practical modeling tasks.
This chapter corresponds to talks given at the Cost 277 summerschool at IIASS in Vietri sul Mare (IT), in Sept. 2004. We would sincerely like to thank Anna Esposito for organizing the summerschool, and for her patience editing this publication.
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Kubin, G., Lainscsek, C., Rank, E. (2005). Identification of Nonlinear Oscillator Models for Speech Analysis and Synthesis. In: Chollet, G., Esposito, A., Faundez-Zanuy, M., Marinaro, M. (eds) Nonlinear Speech Modeling and Applications. NN 2004. Lecture Notes in Computer Science(), vol 3445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11520153_5
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