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By Peter Ladefoged

The straightforward to appreciate process builds at the fundamentals, starting with technical phrases required for describing speech and transcription symbols ahead of relocating directly to the phonetics of English and different languages.

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Additional info for A Course in Phonetics, Fourth edition

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These include the earliest time t at which the autocorrelation drops to a fraction of its initial value [36] or has a point of inflection [37]. These definitions seek to find times where linear correlations between different points in the time series are negligible, but they do not rule out the possibility of more general correlations. Fraser and Swinney [3S] argue that a better value for t is the value that corresponds to the first local minimum of the mutual information where the mutual information is a measure of how much information can be predicted about one time series point given full information about the other.

Swinney, Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33:11341139, 1986. [39] W. Liebert and H. G. Schuster, Proper choice of the time delay for the analysis of chaotic time series. Phys. Lett. A 142:107, 1989. [40] A. M. Albano, A. Passamante, and M. E. Farrell, Using higher-order correlations to define an embedding window. Physica D 54:85, 1991. [41] J. M. Martinerie, A. M. Albano, A. I. Mees, and P. E. Rapp, Mutual information, strange attractors, and the optimal estimation of dimension.

Scand. J . Stat. 22:399-445, 1995. [2] T. Mullin, A dynamical systems approach to time series analysis. In The Nature of Chaos, T. ), Chap. 2. Oxford: Oxford University Press, 1993. [3] V. Isham, Statistical aspects of chaos: a review. E. Barndorff-Nielsen, J. L. Jensen, and W. S. ), Chap. 3. London: Chapman & Hall, 1993. [4] M. Casdagli, Chaos and deterministic versus stochastic non-linear modelling. J . R. Stat. SOC. B. 541303-328, 1992. [5] N. Gershenfeld, An experimentalist’s introduction to the observation of dynamical systems.

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