Improving Intelligibility of Non-Native Speech with Computer-Assisted Phonological Training

Five native speakers of Mandarin Chinese who speak English as a second language, and a control subject, participated in a 15-hour training regime practicing pronunciation of six phonemic contrasts. Subjects were recorded reading a list of isolated English words, randomly ordered from minimal pairs drawn from the six contrasts, the list including words which would appear in the training and words which would be absent from training. The training was computer-based and did not involve corrective responses from an instructor. The trainees spoke into a microphone, providing input to a computerized speech recognizer that evaluated the pronunciation and provided feedback to the trainee. Following the training period, all six speakers were again recorded reading the wordlist. Native speakers of English were asked to identify each token as one or the other member of a minimal pair presented in a forced choice task. Correct identifications of pre-training tokens were at chance level, regardless of speaker, contrast, or whether the word had been included in or excluded from the training program. Correct identifications of post-training tokens averaged 89% over all speakers, all contrasts and regardless of inclusion or exclusion of a token in the training program. The results indicate that production of segments in isolated words can effectively be trained via computer-based administration and that such training generalizes to the same phonological contrast in non-trained words.