Analysis and classification of speech signals by generalized fractal dimension features

Pitsikalis, V; Maragos, P

dc.contributor.author	Pitsikalis, V	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T01:29:51Z
dc.date.available	2014-03-01T01:29:51Z
dc.date.issued	2009	en
dc.identifier.issn	0167-6393	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19371
dc.subject	Broad class phoneme classification	en
dc.subject	Feature extraction	en
dc.subject	Generalized fractal dimensions	en
dc.subject.classification	Acoustics	en
dc.subject.classification	Communication	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Language & Linguistics	en
dc.subject.other	Broad class phoneme classification	en
dc.subject.other	Classification of speech	en
dc.subject.other	Feature vectors	en
dc.subject.other	Fractal feature	en
dc.subject.other	Fractal theory	en
dc.subject.other	Generalized fractal dimensions	en
dc.subject.other	Mel-frequency cepstral coefficients	en
dc.subject.other	Non-linear signal processing	en
dc.subject.other	Phase spaces	en
dc.subject.other	Phoneme classification	en
dc.subject.other	Qualitative aspects	en
dc.subject.other	Raw measurements	en
dc.subject.other	Spectral content	en
dc.subject.other	Speech signals	en
dc.subject.other	Speech sounds	en
dc.subject.other	Statistical parameters	en
dc.subject.other	Dynamical systems	en
dc.subject.other	Fractal dimension	en
dc.subject.other	Initiators (chemical)	en
dc.subject.other	Linguistics	en
dc.subject.other	Signal processing	en
dc.subject.other	Feature extraction	en
dc.title	Analysis and classification of speech signals by generalized fractal dimension features	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.specom.2009.06.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.specom.2009.06.005	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	We explore nonlinear signal processing methods inspired by dynamical systems and fractal theory in order to analyze and characterize speech sounds. A speech signal is at first embedded in a multidimensional phase-space and further employed for the estimation of measurements related to the fractal dimensions. Our goals are to compute these raw measurements in the practical cases of speech signals, to further utilize them for the extraction of simple descriptive features and to address issues on the efficacy of the proposed features to characterize speech sounds. We observe that distinct feature vector elements obtain values or show statistical trends that on average depend on general characteristics such as the voicing, the manner and the place of articulation of broad phoneme classes. Moreover the way that the statistical parameters of the features are altered as an effect of the variation of phonetic characteristics seem to follow some roughly formed patterns. We also discuss some qualitative aspects concerning the linear phoneme-wise correlation between the fractal features and the commonly employed mel-frequency cepstral coefficients (MFCCs) demonstrating phonetic cases of maximal and minimal correlation. In the same context we also investigate the fractal features' spectral content, in terms of the most and least correlated components with the MFCC. Further the proposed methods are examined under the light of indicative phoneme classification experiments. These quantify the efficacy of the features to characterize broad classes of speech sounds. The results are shown to be comparable for some classification scenarios with the corresponding ones of the MFCC features. (C) 2009 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Speech Communication	en
dc.identifier.doi	10.1016/j.specom.2009.06.005	en
dc.identifier.isi	ISI:000274888800005	en
dc.identifier.volume	51	en
dc.identifier.issue	12	en
dc.identifier.spage	1206	en
dc.identifier.epage	1223	en