A new approach for automatic tone error detection in strong accented Mandarin based on dominant set

In this paper, we proposed a new approach based on dominant set [1] for tone error detection in strong accented Mandarin. First, the final boundary generated from forced alignment is regulated by the F0 contour in order to locate the final domain more accurately. After that, proper normalization techniques are explored for the tone features. Finally, clustering and classification methods based on dominant set are utilized for the tone error detection. The proposed approach is tested in comparison with the traditional k-means based method, experimental results show that it achieves more satisfying performance with an average Cross-Correlation 0.84 between human and machine, reaches to that between humans, which have verified the effectiveness of the proposed approach. The main advantage of this approach lies in not only the error pronunciation of tone can be well identified, but also the F0 pattern of the tone error can be informatively provided as the feedback.