By Gerard Chollet, Anna Esposito, Marcos Faundez-Zanuy, Maria Marinaro
This publication offers the revised educational lectures given on the foreign summer season college on Nonlinear Speech Processing-Algorithms and research held in Vietri sul Mare, Salerno, Italy in September 2004.
The 14 revised instructional lectures by means of prime foreign researchers are prepared in topical sections on facing nonlinearities in speech indications, acoustic-to-articulatory modeling of speech phenomena, information pushed and speech processing algorithms, and algorithms and types in response to speech belief mechanisms. in addition to the educational lectures, 15 revised reviewed papers are incorporated providing unique learn effects on job orientated speech applications.
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Additional resources for Nonlinear Speech Modeling and Applications: Advanced Lectures and Revised Selected Papers
2 10 1 MSE 10 0 Backpropagation 10 Elman Levenberg-Marquardt -1 10 0 200 400 600 800 1000 1200 epochs 1400 1600 1800 2000 Fig. 8. Comparison of the Mean Square Error (MSE) versus number of epochs for Elman network and MLP trained with backpropagation and Levenberg-Marquardt Our study has revealed the fast convergence and better performance of the levenberg-Marquardt algorithm. Thus, we have selected it, which computes the approximate Hessian matrix [27-28]. We also apply a multi-start algorithm .
23. : Nonlinear prediction of speech signals using radial basis function networks. Signal Processing VIII: Theories and applications EUSIPCO-1996. 459-462. 24. : Adaptive Hybrid Speech coding with a MLP/LPC structure". IWANN 1999, Lecture notes in computer Science, LNCS 1607 vol. 814-823 25. : Global optimization for neural network training. 45-54. 26. : Neural networks for pattern recognition. Ed. Clarendon Press. 1995 27. Foresee, F. , Hagan, M. 1930-1935, 1997. 28. Mackay, D. J. : Bayesian interpolation.
Acknowledgement This work has been supported by FEDER and the Spanish grant MCYT TIC200308382-C05-02. I want to acknowledge the European project COST-277 “nonlinear speech processing”, that has been acting as a catalyzer for the development of nonlinear speech processing since middle 2001. I also want to acknowledge Prof. Enric Monte-Moreno for the support and useful discussions of these years. References 1. : Linear prediction: a tutorial review. 561580, april 1975 2. : Artificial neural networks: a tutorial.