Abstract
A pronunciation error detection method based on pronunciation error clustering is proposed for computer-assisted language learning (CALL) systems. The method uses a decision-tree-based clustering algorithm, which automatically generates a decision tree from a large number of speech samples, in the mispronunciation rules. The acoustic analysis is conducted by using English and Japanese hidden Markov models (HMM), both of them are gender-dependent monophones with single Gaussian distribution functions. The method, by using different threshold for each cluster, provides marked improvement in pronunciation error detection.
Original language | English |
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Pages (from-to) | 131-133 |
Number of pages | 3 |
Journal | Acoustical Science and Technology |
Volume | 28 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2007 |
Keywords
- CALL system
- Clustering
- Decision tree
- Pronunciation error detection
ASJC Scopus subject areas
- Acoustics and Ultrasonics