Phonological feature-based speech recognition system for pronunciation training in non-native language learning.

Abstract

The authors address the question whether phonological features can be used effectively in an automatic speech recognition (ASR) system for pronunciation training in non-native language (L2) learning. Computer-aided pronunciation training consists of two essential tasks-detecting mispronunciations and providing corrective feedback, usually either on the basis of full words or phonemes. Phonemes, however, can be further disassembled into phonological features, which in turn define groups of phonemes. A phonological feature-based ASR system allows the authors to perform a sub-phonemic analysis at feature level, providing a more effective feedback to reach the acoustic goal and perceptual constancy. Furthermore, phonological features provide a structured way for analysing the types of errors a learner makes, and can readily convey which pronunciations need improvement. This paper presents the authors implementation of such an ASR system using deep neural networks as an acoustic model, and its use for detecting mispronunciations, analysing errors, and rendering corrective feedback. Quantitative as well as qualitative evaluations are carried out for German and Italian learners of English. In addition to achieving high accuracy of mispronunciation detection, the system also provides accurate diagnosis of errors.