Efficient Learning of Articulatory Models Based on Multi-Label Training and Label Correction for Pronunciation Learning
Richeng Duan, Tatsuya Kawahara, Masatake Dantsuji, Hiroaki Nanjo
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.
BibTeX
@inproceedings{icassp2018_efficientlearnin,
title = {Efficient Learning of Articulatory Models Based on Multi-Label Training and Label Correction for Pronunciation Learning},
author = {Richeng Duan and Tatsuya Kawahara and Masatake Dantsuji and Hiroaki Nanjo},
booktitle = {ICASSP 2018},
year = {2018}
}