  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 non-Gaussianity 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/non-Gaussian
dynamics. This supports the use of appropriate hybrid
linear/nonlinear/non-Gaussian 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.
