Literature DB >> 11248981

Discrimination of non-native consonant contrasts varying in perceptual assimilation to the listener's native phonological system.

C T Best1, G W McRoberts, E Goodell.   

Abstract

Classic non-native speech perception findings suggested that adults have difficulty discriminating segmental distinctions that are not employed contrastively in their own language. However, recent reports indicate a gradient of performance across non-native contrasts, ranging from near-chance to near-ceiling. Current theoretical models argue that such variations reflect systematic effects of experience with phonetic properties of native speech. The present research addressed predictions from Best's perceptual assimilation model (PAM), which incorporates both contrastive phonological and noncontrastive phonetic influences from the native language in its predictions about discrimination levels for diverse types of non-native contrasts. We evaluated the PAM hypotheses that discrimination of a non-native contrast should be near-ceiling if perceived as phonologically equivalent to a native contrast, lower though still quite good if perceived as a phonetic distinction between good versus poor exemplars of a single native consonant, and much lower if both non-native segments are phonetically equivalent in goodness of fit to a single native consonant. Two experiments assessed native English speakers' perception of Zulu and Tigrinya contrasts expected to fit those criteria. Findings supported the PAM predictions, and provided evidence for some perceptual differentiation of phonological, phonetic, and nonlinguistic information in perception of non-native speech. Theoretical implications for non-native speech perception are discussed, and suggestions are made for further research.

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Year:  2001        PMID: 11248981      PMCID: PMC2777975          DOI: 10.1121/1.1332378

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  41 in total

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  69 in total

1.  Across-talker effects on non-native listeners' vowel perception in noise.

Authors:  Tessa Bent; Diane Kewley-Port; Sarah Hargus Ferguson
Journal:  J Acoust Soc Am       Date:  2010-11       Impact factor: 1.840

2.  Perception of speech produced by native and nonnative talkers by listeners with normal hearing and listeners with cochlear implants.

Authors:  Caili Ji; John J Galvin; Yi-ping Chang; Anting Xu; Qian-Jie Fu
Journal:  J Speech Lang Hear Res       Date:  2014-04-01       Impact factor: 2.297

3.  Do adults with cochlear implants rely on different acoustic cues for phoneme perception than adults with normal hearing?

Authors:  Aaron C Moberly; Joanna H Lowenstein; Eric Tarr; Amanda Caldwell-Tarr; D Bradley Welling; Antoine J Shahin; Susan Nittrouer
Journal:  J Speech Lang Hear Res       Date:  2014-04-01       Impact factor: 2.297

4.  Training a non-native vowel contrast with a distributional learning paradigm results in improved perception and production.

Authors:  Heather Kabakoff; Gretchen Go; Susannah V Levi
Journal:  J Phon       Date:  2019-12-13

5.  Syllable structure and integration of voicing and manner of articulation information in labial consonant identification.

Authors:  Noah H Silbert
Journal:  J Acoust Soc Am       Date:  2012-05       Impact factor: 1.840

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Authors:  Annie J Olmstead; Navin Viswanathan
Journal:  Psychon Bull Rev       Date:  2018-04

7.  Category labels induce boundary-dependent perceptual warping in learned speech categories.

Authors:  Kristen Swan; Emily Myers
Journal:  Second Lang Res       Date:  2013-10-01

8.  LANGUAGE- AND TALKER-DEPENDENT VARIATION IN GLOBAL FEATURES OF NATIVE AND NON-NATIVE SPEECH.

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Authors:  Begoña Díaz; Cristina Baus; Carles Escera; Albert Costa; Núria Sebastián-Gallés
Journal:  Proc Natl Acad Sci U S A       Date:  2008-10-13       Impact factor: 11.205

10.  Sleep and native language interference affect non-native speech sound learning.

Authors:  F Sayako Earle; Emily B Myers
Journal:  J Exp Psychol Hum Percept Perform       Date:  2015-08-17       Impact factor: 3.332

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