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V. Despotovic, N. Görtz, Z. Peric:
"Improved non-linear long-term predictors based on Volterra filters";
Vortrag: ELMAR 2012, Zadar, Croatia; 12.09.2012 - 14.09.2012; in: "Proceedings ELMAR 2012", IEEE Xplore, (2012), ISBN: 978-1-4673-1243-1; S. 231 - 234.

Speech prediction is extensively based on linear models. However, components generated by nonlinear effects are also contained in speech signals, which is neglected using linear techniques. This paper presents long-term nonlinear predictor based on second-order Volterra filters that is shown to be superior to linear long-term predictor with only a minimal increase in complexity and the number of coefficients. It can be used connected in cascade with short-term linear predictor. The frame/subframe structure is proposed, where each frame is divided into four subframes. Second order Volterra long-term prediction is applied to each subframe separately.

Speech prediction is extensively based on linear models. However, components generated by nonlinear effects are also contained in speech signals, which is neglected using linear techniques. This paper presents long-term nonlinear predictor based on second-order Volterra filters that is shown to be superior to linear long-term predictor with only a minimal increase in complexity and the number of coefficients. It can be used connected in cascade with short-term linear predictor. The frame/subframe structure is proposed, where each frame is divided into four subframes. Second order Volterra long-term prediction is applied to each subframe separately.

Nonlinear signal processing , Pitch , Speech prediction , Volterra filters

http://publik.tuwien.ac.at/files/PubDat_211710.pdf