title: Improvement of speech recognition by nonlinear noise reduction.
abstract: The success of nonlinear noise reduction applied to a single channel recording of human voice is measured in terms of the recognition rate of a commercial speech recognition program in comparison to the optimal linear filter. The overall performance of the nonlinear method is shown to be superior. We hence demonstrate that an algorithm that has its roots in the theory of nonlinear deterministic dynamics possesses a large potential in a realistic application.
authors.affiliation: Max Planck Institute for Physics of Complex Systems, Nöthnitzer Strasse 38, D-01187 Dresden, Germany. urbanow@pks.mpg.de
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Urbanowicz
authors.forename: Holger
authors.initials: H
authors.lastname: Kantz
mesh_terms: D000465:Algorithms; D006801:Humans; D016014:Linear Models; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D049250:Speech Recognition Software / Q000706:statistics & numerical data*