Training Japanese Listeners to Perceive American English Vowels: Influence of Training Sets
| Autor: | Diane Kewley-Port, Kanae Nishi |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Adult
Male Consonant Linguistics and Language Time Factors Speech perception Teaching media_common.quotation_subject Culture American English Linguistics Training (civil) Language and Linguistics Speech and Hearing Asian People Phonetics Perception Speech Perception Humans Female Psychology Neuroscience of multilingualism Language media_common |
| Zdroj: | Journal of Speech, Language, and Hearing Research. 50:1496-1509 |
| ISSN: | 1558-9102 1092-4388 |
| DOI: | 10.1044/1092-4388(2007/103) |
| Popis: | Purpose Studies on speech perception training have shown that adult 2nd language learners can learn to perceive non-native consonant contrasts through laboratory training. However, research on perception training for non-native vowels is still scarce, and none of the previous vowel studies trained more than 5 vowels. In the present study, the influence of training set sizes was investigated by training native Japanese listeners to identify American English (AE) vowels. Method Twelve Japanese learners of English were trained 9 days either on 9 AE monophthongs (fullset training group) or on the 3 more difficult vowels (subset training group). Five listeners served as controls and received no training. Performance of listeners was assessed before and after training as well as 3 months after training was completed. Results Results indicated that (a) fullset training using 9 vowels in the stimulus set improved average identification by 25%; (b) listeners in both training groups generalized improvement to untrained words and tokens spoken by novel speakers; and (c) both groups maintained improvement after 3 months. However, the subset group never improved on untrained vowels. Conclusions Training protocols for learning non-native vowels should present a full set of vowels and should not focus only on the more difficult vowels. |
| Databáze: | OpenAIRE |
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