Abstract
Automatic pronunciation scoring makes novel applications for computer assisted language learning possible. In this paper we concentrate on the feature extraction. A relatively large feature vector with 28 sentence- and 33 word-level features has been designed. On the word-level correctly and mispronounced words are classified, on the sentence-level utterances are rated with 5 discrete marks. The features are evaluated on two databases with non-native adults’ and children’s speech, respectively. Up to 72 % class-wise-averaged recognition rate is achieved for 2 classes; the result of the 5-class problem can be interpreted as 80 % recognition rate.
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© 2005 Springer-Verlag Berlin Heidelberg
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Hacker, C., Cincarek, T., Gruhn, R., Steidl, S., Nöth, E., Niemann, H. (2005). Pronunciation Feature Extraction. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_18
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DOI: https://doi.org/10.1007/11550518_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28703-2
Online ISBN: 978-3-540-31942-9
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