Improved non-linear long-term predictors based on Volterra filters

Vladimir Despotović*, Norbert Görtz, Zoran Perić

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings ELMAR-2012 - 54th International Symposium ELMAR-2012
Number of pages4
Publication statusPublished - 2012
Externally publishedYes
Event54th International Symposium ELMAR-2012 - Zadar, Croatia
Duration: 12 Sept 201214 Sept 2012

Publication series

NameProceedings Elmar - International Symposium Electronics in Marine
ISSN (Print)1334-2630


Conference54th International Symposium ELMAR-2012


  • Nonlinear signal processing
  • Pitch
  • Speech prediction
  • Volterra filters


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