Multi-lingual and multi-task DNN learning for articulatory error detection

摘要

For effective pronunciation error detection for second language learners, we address 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 a multi-lingual learning method, in which the speech database of the target language (L2) and the native language (L1) of the learners are combined. We also investigate multi-task learning methods. These methods are applied to Mandarin Chinese pronunciation learning by Japanese native speakers. Effects of the multi-lingual and multi-task learning methods are demonstrated in the attribute classification of native speech and pronunciation error detection for non-native speech.