Testing the assumptions of linear prediction analysis in normal vowels

• Published in: The Journal of the Acoustical Society of America Vol. 119; no. 1; pp. 549 - 558
• Main Authors: LITTLE, M. A, MCSHARRY, P. E, MOROZ, I. M, ROBERTS, S. J
• Format: Journal Article
• Language: English
• Published: Woodbury, NY Acoustical Society of America 2006; American Institute of Physics
• Subjects: Acoustic signal processing; Acoustics; Exact sciences and technology; Female; Fourier Analysis; Fundamental areas of phenomenology (including applications); Humans; Male; Mathematical Computing; Nonlinear Dynamics; Normal Distribution; Physics; Sound Spectrography; Speech Acoustics; Time Factors; Voice - physiology; Experimental study; Vowel; Linear prediction
• ISSN: 0001-4966; 1520-8524
• DOI: 10.1121/1.2141266

In this paper we develop an improved surrogate data test to show experimental evidence, for all the simple vowels of U.S. 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 modeling 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 modeling. 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.