Investigating automatic recognition of non-native children's speech
Matteo Gerosa, Diego Giuliani · 2004
This paper presents an initial effort in the area of non-native children’s speech recognition by exploiting two children databases, one consisting of speech collected from native En-glish children, the other one consisting of English sentences read by Italian learners of English in the same age range of the native speakers. First, a baseline speech recognizer for British English was trained on the corpus of native speech and ap-plied to recognize native and non-native speech. Word error rates achieved for Italian children were 100%-600 % higher than those achieved for native English children of the same age. By using a small amount of non-native speech from a group of Ital-ian learners of English, acoustic models were adapted to this particular category of speakers. Adaptation of both context-independent and context-dependent HMMs showed to greatly improve recognition performance on non-native speech, obtain-ing relative reductions in word error rate up to 66.2%. 1.