Training humans in non-native phoneme perception using a monkey psychoacoustic procedure
Autor: | Joan M. Sinnott, Christopher L. Gonzales, Ambrin F. Masood, Toshimasa Ishihara |
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Rok vydání: | 2007 |
Předmět: |
medicine.medical_specialty
Acoustics and Ultrasonics media_common.quotation_subject Acoustics Audiology behavioral disciplines and activities Feedback Arts and Humanities (miscellaneous) Hearing Japan Phonetics Vowel Perception medicine Animals Humans Psychoacoustics media_common Language Context effect Contrast (statistics) Recognition Psychology Haplorhini Speech processing United States Categorization Models Animal Percept Psychology psychological phenomena and processes |
Zdroj: | The Journal of the Acoustical Society of America. 121(6) |
ISSN: | 1520-8524 |
Popis: | Humans were trained to categorize problem non-native phonemes using an animal psychoacoustic procedure that trains monkeys to greater than 90% correct in phoneme identification [Sinnott and Gilmore, Percept. Psychophys. 66, 1341-1350 (2004)]. This procedure uses a manual left versus right response on a lever, a continuously repeated stimulus on each trial, extensive feedback for errors in the form of a repeated correction procedure, and training until asymptotic levels of performance. Here, Japanese listeners categorized the English liquid contrast /r-l/, and English listeners categorized the Middle Eastern dental-retroflex contrast /d-D/. Consonant-vowel stimuli were constructed using four talkers and four vowels. Native listeners and phoneme contrasts familiar to all listeners were included as controls. Responses were analyzed using percent correct, response time, and vowel context effects as measures. All measures indicated nativelike Japanese perception of /r-l/ after 32 daily training sessions, but this was not the case for English perception of /d-D/. Results are related to the concept of "robust" (more easily recovered) versus "fragile" (more easily lost) phonetic contrasts [Burnham, Appl. Psycholing. 7, 207-240 (1986)]. |
Databáze: | OpenAIRE |
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