4 accepted papers
The majority of the current mispronunciation detection and diagnosis (MD&D) methods rely on manually annotated data for model training. However, annotating mispronunciations produced by second language (L2) learners is costly. Consequently, data scarcity emerges as a significant challenge in MD&D ta…
Voice conversion(VC) model aims to convert the source timbre to the target one. Recently, many VC models utilize pre-trained models to enhance the performance and achieve good results. However, pre-trained models could not somehow disentangle the timbre and linguistic information, thus resulting in…
Traditionally, the performance of non-native mispronunciation verification systems relied on effective phone-level labelling of non-native corpora. In this study, a multi-view approach is proposed to incorporate discriminative feature representations which requires less annotation for non-native mis…
L2 learners of Mandarin have difficulty learning native-like pronunciation of nasal codas. In order to help them learn native-like pronunciation, we propose to develop targeted classifiers for automatic pronunciation error detection. In this paper, perceptual experiments with modified speech are des…