Paper Info
Title
Non-linear speech representation based on local predictability exponents
Abstract
Looking for new perspectives to analyze non-linear dynamics of speech, this paper presents a novel approach based on a microcanonical multiscale formulation which allows the geometric and statistical description of multiscale properties of the complex dynamics. Speech is a complex system whose dynamics can be, to some extent, geometrically and statistically accessed by the computation of Local Predictability Exponents (LPEs) unlocking the determination of the most informative subset (Most Singular Manifold or MSM), leading to associated compact representation and reconstruction. But the complex intertwining of different dynamics in speech (added to purely turbulent descriptions) suggests the definition of appropriate multiscale functionals that might influence the evaluation of LPEs, hence leading to more compact MSM. Consequently, by using the classical and generic Sauer/Allebach algorithm for signal reconstruction from irregularly spaced samples, we show that speech reconstruction of good quality can be achieved using MSM of low cardinality. Moreover, in order to further show the potential of the new methodology, we develop a simple and efficient waveform coder which achieves almost the same level of perceptual quality as a standard coder, while having a lower bit-rate.
Year
DOI
Venue
2014
10.1016/j.neucom.2012.12.061
Neurocomputing
Keywords
DocType
Volume
signal reconstruction,appropriate multiscale functionals,microcanonical multiscale formulation,complex system,complex intertwining,compact representation,speech reconstruction,local predictability exponent,complex dynamic,non-linear speech representation,multiscale property,compact msm
Journal
132,
ISSN
Citations 
PageRank 
0925-2312
2
0.37
References 
Authors
8
5
Name
Order
Citations
PageRank
Vahid Khanagha1413.97
Khalid Daoudi214523.68
Oriol Pont3545.95
hussein yahia420.37
Antonio Turiel5242.23