Nonlinear speech analysis using models for chaotic systems

Kokkinos, I; Maragos, P

dc.contributor.author	Kokkinos, I	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T01:22:49Z
dc.date.available	2014-03-01T01:22:49Z
dc.date.issued	2005	en
dc.identifier.issn	1063-6676	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16672
dc.subject	Chaos	en
dc.subject	Nonlinear systems	en
dc.subject	Speech analysis	en
dc.subject.classification	Acoustics	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Chaotic systems	en
dc.subject.other	Phase space	en
dc.subject.other	Scalar signal	en
dc.subject.other	Speech signals	en
dc.subject.other	Chaos theory	en
dc.subject.other	Computer simulation	en
dc.subject.other	Distributed computer systems	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Signal processing	en
dc.subject.other	Speech processing	en
dc.subject.other	Speech analysis	en
dc.title	Nonlinear speech analysis using models for chaotic systems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TSA.2005.852982	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TSA.2005.852982	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	In this paper, we use concepts and methods from chaotic systems to model and analyze nonlinear dynamics in speech signals. The modeling is done not on the scalar speech signal, but on its reconstructed multidimensional attractor by embedding the scalar signal into a phase space. We have analyzed and compared a variety of nonlinear models for approximating the dynamics of complex systems using a small record of their observed output. These models include approximations based on global or local polynomials as well as approximations inspired from machine learning such as radial basis function networks, fuzzy-logic systems and support vector machines. Our focus has been on facilitating the application of the methods of chaotic signal analysis even when only a short time series is available, like phonemes in speech utterances. This introduced an increased degree of difficulty that was dealt with by resorting to sophisticated function approximation models that are appropriate for short data sets. Using these models enabled us to compute for short time series of speech sounds useful features like Lyapunov exponents that are used to assist in the characterization of chaotic systems. Several experimental insights are reported on the possible applications of such nonlinear models and features. © 2005 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Speech and Audio Processing	en
dc.identifier.doi	10.1109/TSA.2005.852982	en
dc.identifier.isi	ISI:000232734500002	en
dc.identifier.volume	13	en
dc.identifier.issue	6	en
dc.identifier.spage	1098	en
dc.identifier.epage	1109	en