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  • Title: Static, dynamic, and relational properties in vowel perception.
    Author: Nearey TM.
    Journal: J Acoust Soc Am; 1989 May; 85(5):2088-113. PubMed ID: 2659638.
    Abstract:
    The present work reviews theories and empirical findings, including results from two new experiments, that bear on the perception of English vowels, with an emphasis on the comparison of data analytic "machine recognition" approaches with results from speech perception experiments. Two major sources of variability (viz., speaker differences and consonantal context effects) are addressed from the classical perspective of overlap between vowel categories in F1 x F2 space. Various approaches to the reduction of this overlap are evaluated. Two types of speaker normalization are considered. "Intrinsic" methods based on relationships among the steady-state properties (F0, F1, F2, and F3) within individual vowel tokens are contrasted with "extrinsic" methods, involving the relationships among the formant frequencies of the entire vowel system of a single speaker. Evidence from a new experiment supports Ainsworth's (1975) conclusion [W. Ainsworth, Auditory Analysis and Perception of Speech (Academic, London, 1975)] that both types of information have a role to play in perception. The effects of consonantal context on formant overlap are also considered. A new experiment is presented that extends Lindblom and Studdert-Kennedy's finding [B. Lindblom and M. Studdert-Kennedy, J. Acoust. Soc. Am. 43, 840-843 (1967)] of perceptual effects of consonantal context on vowel perception to /dVd/ and /bVb/ contexts. Finally, the role of vowel-inherent dynamic properties, including duration and diphthongization, is briefly reviewed. All of the above factors are shown to have reliable influences on vowel perception, although the relative weight of such effects and the circumstances that alter these weights remain far from clear. It is suggested that the design of more complex perceptual experiments, together with the development of quantitative pattern recognition models of human vowel perception, will be necessary to resolve these issues.
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