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Abstract

This paper analyses the changes in transfer characteristics of the vocal tract when closed by a mask, i.e. a chamber. The analysis was performed in two ways: by analytical estimation and by measurements in the vocal tract physical model for the case of mask with inner volume V = 430 cm3, corresponding to the oxygen masks used in combat airplanes. It was shown that closing the vocal tract with a mask cavity increases the first formant frequency by about 10% in front and high vowels (/e/, /i/, and /u/) and the frequencies of the first two formants by about 5% in the remaining two vowels (/a/ and /o/). It was also revealed that longitudinal and transversal resonances in the mask chamber can lead to errors in the recognition of the vowel formant frequencies. The results point to the need for additional knowledge about resonances in mask application.
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Abstract

A vocal tract model based on a digital waveguide is presented in which the vocal tract has been decomposed into uniform cylindrical segments of variable lengths. We present a model for the real-time numerical solution of the digital waveguide equations in a uniform tube with the temporally varying cross section. In the current work, the uniform cylindrical segments of the vocal tract may have their different lengths, the time taken by the sound wave to propagate through a cylindrical segment in an axial direction may not be an integer multiple of each other. In such a case, the delay in an axial direction is necessarily a fractional delay. For the approximation of fractional-delay filters, Lagrange interpolation is used in the current model. Variable length of the individual segment of the vocal tract enables the model to produce realistic results. These results are validated with accurate benchmark model. The proposed model has been devised to elongate or shorten any arbitrary cylindrical segment by a suitable scaling factor. This model has a single algorithm and there is no need to make section of segments for elongation or shortening of the intermediate segments. The proposed model is about 23% more efficient than the previous model.
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