Index of content:
Volume 119, Issue 1, January 2006
- SPEECH PRODUCTION 
119(2006); http://dx.doi.org/10.1121/1.2133491View Description Hide Description
Intraglottal pressure distributions depend upon glottal shape, size, and diameter. This study reports the effects of varying glottal angle on intraglottal and transglottal pressures using a three-dimensional Plexiglas™ model with a glottis having nine symmetric glottal angles and a constant minimal glottal diameter of . The empirical data were supported by computational results using FLUENT. The results suggested that (1) the greater the convergent glottal angle, the greater outward driving forces (higher intraglottal pressures) on the vocal folds; (2) flow resistance was greatest for the uniform glottis, and least for the 10° divergent glottis; (3) the greatest negative pressure in the glottis and therefore the greatest pressure recovery for diverging glottal shapes occurred for an angle of 10°; (4) the smaller the convergent angle, the greater the flow resistance; (5) FLUENT was highly accurate in predicting the empirical pressures of this model; (6) flow separation locations (given by FLUENT) for the divergent glottis moved upstream for larger flows and larger glottal angles. The results suggest that phonatory efficiency related to aerodynamics may be enhanced with vocal fold oscillations that include large convergent angles during glottal opening and small (5°–10°) divergent angles during glottal closing.
119(2006); http://dx.doi.org/10.1121/1.2141266View Description Hide Description
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 predictionanalysis, a ubiquitous technique in current speech technologies, cannot be used to extract all the dynamical structure of real speechtime 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 modelingspeechtime 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.