Little, Max and McSharry, Patrick E. and Moroz, Irene M. and Roberts, Stephen J. (2006) Testing the assumptions of linear prediction analysis in normal vowels. Journal of the Acoustical Society of America, 119 (1). pp. 549558.

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Official URL: http://link.aip.org/link/?JAS/119/549/1
Abstract
This paper develops an improved surrogate data test to show experimental evidence, for all the simple vowels of US English, for both male and female speakers, that Gaussian linear prediction analysis, a ubiquitous technique in current speech technologies, cannot be used to extract all the dynamical structure of real speech time series. The test provides robust evidence undermining the validity of these linear techniques, supporting the assumptions of either dynamical nonlinearity and/or nonGaussianity common to more recent, complex, efforts at dynamical modelling speech time series. However, an additional finding is that the classical assumptions cannot be ruled out entirely, and plausible evidence is given to explain the success of the linear Gaussian theory as a weak approximation to the true, nonlinear/nonGaussian dynamics. This supports the use of appropriate hybrid linear/nonlinear/nonGaussian modelling. With a calibrated calculation of statistic and particular choice of experimental protocol, some of the known systematic problems of the method of surrogate data testing are circumvented to obtain results to support the conclusions to a high level of significance.
Item Type:  Article 

Subjects:  D  G > Dynamical systems and ergodic theory O  Z > Probability theory and stochastic processes 
Research Groups:  Oxford Centre for Industrial and Applied Mathematics 
ID Code:  214 
Deposited By:  Max Little 
Deposited On:  05 Jan 2006 
Last Modified:  29 May 2015 18:18 
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