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Efficient Learning of Articulatory Models Based on Multi-Label Training and Label Correction for Pronunciation Learning | IEEE Conference Publication | IEEE Xplore

Efficient Learning of Articulatory Models Based on Multi-Label Training and Label Correction for Pronunciation Learning


Abstract:

Articulatory feedback is effective for computer-assisted pronunciation training (CAPT) systems. This paper investigates efficient model learning methods for providing art...Show More

Abstract:

Articulatory feedback is effective for computer-assisted pronunciation training (CAPT) systems. This paper investigates efficient model learning methods for providing articulatory information to language learners. We first propose an articulatory attribute modeling method based on a multi-label learning scheme. Then, the models are further enhanced with a simple and effective training label correction method. These proposed methods are evaluated in three tasks: native attribute recognition, pronunciation error detection of non-native speech, and non-native speech recognition. Experimental results show that proposed methods significantly improve the conventional deep neural network (DNN) based articulatory models.
Date of Conference: 15-20 April 2018
Date Added to IEEE Xplore: 13 September 2018
ISBN Information:
Electronic ISSN: 2379-190X
Conference Location: Calgary, AB, Canada

References

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