A preliminary exploration on tone error detection in Mandarin based on clustering

This paper addresses the ongoing issue of tone error detection for Mandarin Computer Assisted Language Learning (CALL) systems. A novel approach based on clustering is proposed. The selection of different contextual tonal factors including Uni-tone, LBi-tone and RBi-tone are explored. Experimental results show that our proposed approach is feasible, obtaining an Equal Error Rate (EER) of 18.75% by LBi-tone and with a 2.35% reduction compared to Uni-tone on a real corpus of nonnative speaker of Mandarin. Additionally, effects of contextual tonal factors on disyllabic words are investigated in this work.