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Conference Paper

Timing of perception for all English diphones [Abstract]

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McQueen,  James M.
Language Comprehension Department, MPI for Psycholinguistics, Max Planck Society;
Radboud University;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;

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Cutler,  Anne
Language Comprehension Department, MPI for Psycholinguistics, Max Planck Society;
Radboud University;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;

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Citation

Warner, N. L., McQueen, J. M., Liu, P. Z., Hoffmann, M., & Cutler, A. (2012). Timing of perception for all English diphones [Abstract]. Program abstracts from the 164th Meeting of the Acoustical Society of America published in the Journal of the Acoustical Society of America, 132(3), 1967.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-EF51-B
Abstract
Information in speech does not unfold discretely over time; perceptual cues are gradient and overlapped. However, this varies greatly across segments and environments: listeners cannot identify the affricate in /ptS/ until the frication, but information about the vowel in /li/ begins early. Unlike most prior studies, which have concentrated on subsets of language sounds, this study tests perception of every English segment in every phonetic environment, sampling perceptual identification at six points in time (13,470 stimuli/listener; 20 listeners). Results show that information about consonants after another segment is most localized for affricates (almost entirely in the release), and most gradual for voiced stops. In comparison to stressed vowels, unstressed vowels have less information spreading to neighboring segments and are less well identified. Indeed, many vowels, especially lax ones, are poorly identified even by the end of the following segment. This may partly reflect listeners’ familiarity with English vowels’ dialectal variability. Diphthongs and diphthongal tense vowels show the most sudden improvement in identification, similar to affricates among the consonants, suggesting that information about segments defined by acoustic change is highly localized. This large dataset provides insights into speech perception and data for probabilistic modeling of spoken word recognition.