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Author Oglesbee, Eric ♦ Jong, Kenneth De
Source CiteSeerX
Content type Text
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Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Multi-dimensional Stimulus Space ♦ Search Method ♦ Perceptual Category Center ♦ Phonetic Categorization ♦ Forced Identification ♦ Viable Approach ♦ General Multi-dimensional Search Algorithm ♦ First Approximation ♦ Useful Data ♦ Practical Problem ♦ Inherent Bias ♦ Long-term Bias ♦ Rapid Convergence ♦ Traditional Method ♦ Multi-dimensional Space ♦ Small Number ♦ Multiple Type ♦ Vowel Space ♦ Stimulus Dimension ♦ Adaptive Tracking Algorithm ♦ Entire Stimulus Space ♦ Special Property
Abstract Examining phonetic categorization in multi-dimensional stimulus spaces poses a number of practical problems. The traditional method of forced identification of an entire stimulus space becomes prohibitive when the number and size of stimulus dimensions becomes increasingly large. In response to this, Iverson and Evans (2003) proposed an adaptive tracking algorithm for finding best exemplars of vowels in a multi-dimensional space. Their algorithm converged on best exemplars in a relatively small number of trials; however, the search method took advantage of special properties of the vowel space in order to achieve rapid convergence. In this paper, a more general multi-dimensional search algorithm is proposed and analyzed for inherent biases. Results showed that there are no long-term biases in the search method, and that multiple types of useful data are generated. The proposed search method appears to be a viable approach for generating a first approximation of phonetic categorization in multi-dimensional stimulus spaces. 1 I.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study