Pronunciation Error Detection using DNN Articulatory Model based on Transfer Learning

摘要

Aiming at detecting pronunciation errors produced by second language learners and providing corrective feedbacks related with articulation, we address effective articulatory models based on deep neural network (DNN). Articulatory attributes are defined for manner and place of articulation. In order to efficiently train these models of non-native speech without using such data, which is difficult to collect in a large scale, we propose to exploit large speech corpora of native and target language to model inter-language phenomena. We also investigate closely-related secondary tasks which aim at effective learning of DNN articulatory models. These methods are applied to Mandarin Chinese pronunciation learning by Japanese native speakers. Effects of these methods are confirmed in the native attribute classification and pronunciation error detection of non-native speech.