JOURNAL ARTICLE

Discrimination of uncategorised non-native vowel contrasts is modulated by perceived overlap with native phonological categories

Mona FarisCatherine T. BestMichael D. Tyler

Year: 2018 Journal:   Journal of Phonetics Vol: 70 Pages: 1-19   Publisher: Elsevier BV

Abstract

Non-native vowels perceived as speech-like but not identified with a particular native (L1) vowel are assimilated as uncategorised, and have received very little empirical attention. According to the Perceptual Assimilation Model (PAM: Best, 1995), contrasts where one or both phones are uncategorised are Uncategorised-Categorised and Uncategorised-Uncategorised, respectively. We reasoned that discrimination accuracy for these assimilations should be influenced by perceived phonological overlap (i.e., overlap in the categorisations to L1 vowels), and predicted excellent discrimination for non-overlapping contrasts, followed by partially overlapping, and completely overlapping contrasts. To test those predictions, Australian English speakers discriminated between Danish monophthongal and diphthongal vowel contrasts that formed Uncategorised-Categorised and Uncategorised-Uncategorised assimilations, varying in the presence of overlap, in addition to Two-Category and Single-Category contrasts. The discrimination accuracy results supported our predictions. These findings have implications for PAM, and broader relevance to second-language learning models, as they allow for more precise discrimination predictions to be made based on assimilation type.

Keywords:
Vowel Psychology Australian English Assimilation (phonology) Perception Danish Variation (astronomy) Linguistics Cognitive psychology Speech recognition Computer science

Metrics

45
Cited By
5.21
FWCI (Field Weighted Citation Impact)
51
Refs
0.94
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Phonetics and Phonology Research
Social Sciences →  Psychology →  Experimental and Cognitive Psychology
Linguistic Variation and Morphology
Social Sciences →  Social Sciences →  Linguistics and Language
Speech Recognition and Synthesis
Physical Sciences →  Computer Science →  Artificial Intelligence

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