JOURNAL ARTICLE

Acoustic-phonetic analysis of the acquisition of American English tense-lax contrasts by native speakers of Chinese

Catherine L. Rogers

Year: 1999 Journal:   The Journal of the Acoustical Society of America Vol: 105 (2_Supplement)Pages: 1095-1095   Publisher: Acoustical Society of America

Abstract

In a study of the perception of Chinese-accented English by native American English-speaking listeners [C. Rogers and J. Dalby, J. Acoust. Soc. Am. 100, 2725(A) (1996)], in which performance on seven segmental error category variables was correlated with intelligibility at the sentence level, vowel tenseness was found to be most strongly correlated with connected-speech intelligibility (r=0.76, p<0.001). Analyses of these results across the eight Chinese speakers showed that the average performance of the four most intelligible speakers (measured in terms of sentence percent-correct performance) on words targeting tense-lax contrasts surpassed that of the four least intelligible speakers by more than 30%. Analyses of both durational and spectral properties of the target lax phonemes /ɪ, ε, and ᴜ/ produced by the Chinese speakers will (1) explore the acoustic properties responsible for the difference in performance on the target lax phonemes across the two groups of speakers, (2) compare results across the three target phonemes, and (3) investigate the hypothesis that acquisition of the tense-lax contrasts is associated with more nativelike implementation of phenomena realized only at the connected-speech level (e.g., phrase-final lengthening). [Work supported by NIDCD.]

Keywords:
Intelligibility (philosophy) Sentence Vowel Linguistics Phrase Psychology American English Perception Speech recognition Mathematics Acoustics Computer science Physics

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Topics

Phonetics and Phonology Research
Social Sciences →  Psychology →  Experimental and Cognitive Psychology
Speech Recognition and Synthesis
Physical Sciences →  Computer Science →  Artificial Intelligence
Speech and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing

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