Dialogue Game Considering Articulatory Features for Personalized Computer-Aided Pronunciation Training
Date Issued
2015
Date
2015
Author(s)
Wu, Chuan-Hsun
Abstract
In this thesis we propose a new dialogue game framework considering Articulatory Features (AFs) for personalized Computer-Assisted Language Learning (CALL). We use an automatic pronunciation evaluator and a set of dialogue scripts for reastaurant scenarios, with policy for selecting learning sentence trained by Reinforcement Learning (RL), based on continuous state Markov Decision Process (MDP) as the system’s model, We utilize a corpus of real learner data, including pronunciation Error Patterns (EP) annotated by Mandarin teachers, to train a learner simulation model, in order to produce a huge quantity of simulated learners for MDP training. This thesis proposes a new concept of considering Articulatory Features (AFs) in a dialogue game for Computer-Assisted Language Learning (CALL). In the previous work, the learner has to go through longer dialogue paths (more dialogue turns) to practice some rare and ill-pronounced pronunciation units. Here the new approach is based on an important hypothesis: practicing other pronunciation unitswith highproportion of the same set of AFs of a considered rare unit, taken as ’pseudo practice’, can somehow offer improvement to the pronunciation of the considered rare unit. We further set different weights for different AFs within different pronunciation units, so as to have the system concentrated on those rare or ill-pronounced units. Experimental results verify the feasibility of the proposed framework based on the hypothesis above.
Subjects
Articulatory Feature
Computer-Assisted Language Learning
Dialogue System
Type
thesis
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