Searching for best exemplars in multidimensional stimulus spaces | Zendy

Eric Oglesbee | Zendy; Kenneth De Jong | Zendy

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Searching for best exemplars in multidimensional stimulus spaces

Author(s) -

Eric Oglesbee,

Kenneth De Jong

Publication year - 2007

Publication title -

the journal of the acoustical society of america

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.619

H-Index - 187

eISSN - 1520-8524

pISSN - 0001-4966

DOI - 10.1121/1.2776999

Subject(s) - categorization , stimulus (psychology) , vowel , computer science , speech recognition , algorithm , mathematics , multidimensional analysis , pattern recognition (psychology) , artificial intelligence , cognitive psychology , statistics , psychology

Examining phonetic categorization in multidimensional stimulus spaces poses a number of practical problems. The traditional method of forced identification becomes prohibitive when the number and size of stimulus dimensions becomes increasingly large. In response, Evans and Iverson [J. Acoust. Soc. Am. 115, 352-361 (2004)] proposed an adaptive tracking algorithm for finding vowel best exemplars in a multidimensional space. This algorithm converged on best exemplars in a small number of trials; however, the search method was designed explicitly for vowel stimuli. In this paper, a more general multidimensional search algorithm is described, and results from simulations and experiments using the proposed algorithm are presented.

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