Integrating Articulatory Features into Acoustic-Phonemic Model for Mispronunciation Detection and Diagnosis in L2 English Speech

• Main Authors: Mao, Shaoguang, Wu, Zhiyong, Li, Xu, Li, Runnan, Wu, Xixin, Meng, Helen
• Format: Conference Proceeding
• Language: English
• Subjects: acoustic-phonemic model; Acoustics; articulatory features; Computational modeling; Computer-aided pronunciation training; Hidden Markov models; mispronunciation detection and diagnosis; multi-task learning; Phonetics; Task analysis; Training
• ISSN: 1945-788X
• DOI: 10.1109/ICME.2018.8486462

This paper proposes novel approaches to mispronunciation detection and diagnosis (MDD) on second-language (L2) learners' speech with articulatory features. Here, articulatory features are the positions of articulators when pronouncing phonemes and reflect the pronunciation mechanisms of each phoneme. The use of articulatory features in MDD is helpful in distinguishing phonemes. Three models with articulatory features are proposed based on acoustic-phonemic model (APM): 1) articulatory-acoustic-phonemic model (AAPM) that embeds articulatory features directly into input features; 2) AAPM with feature representation (R-AAPM) to represent original input features with articulatory features; and 3) articulatory multi-task acoustic-phonemic model (A-MT-APM) where phoneme recognizer and articulatory feature classifiers are trained simultaneously in multi-task manner. Compared with baseline phoneme-based APM, proposed approaches perform better in mispronunciation detection and diagnosis measured with Precision, Recall and F1-Measure metrics. Specifically, the A-MT-APM approach gains 5.6% and 7.0% improvement in F1-Measure and diagnostic accuracy respectively. The contributions include: 1) introducing the articulatory features to MDD in deep learning framework; 2) investigating several model architectures for better exploiting articulatory features.