Abstract
The aim of this work is to improve Japanese pronunciation assessment by utilizing pitch information, and the performance of the proposed method is evaluated against several performance measures.Firstly the baseline models are constructed by using MFCCs (Mel-frequency cepstral coefficients) as well as the log energy. The transcriptions are adjusted systematically due to the unique property of Japanese pronunciation. Then we train the improved acoustic models, called pitch-added models, with MFCCs, log energy and pitch. ACF (autocorrelation function) and UPDUDP (unbroken pitch determination using dynamic programming) are adopted as the pitch extraction method to generate a broken pitch contour and an unbroken pitch contour respectively.The performance of the proposed method is evaluated by using ranking-based confidence measure and pronunciation error detection. Experimental results show that the proposed method outperforms the baseline. However, unvoiced phonemes are considered to have no pitch values. It is therefore we try to load the models selectively between the pitch-added models and the original ones, and the experimental results show a slight improvement of the selective approach than the non-selective approach.