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Automatic scene detection is a fundamental step for efficient video searching and browsing. This paper presents our current work on scene detection that integrates three effective strategies into a single framework. For each video, firstly, a coherence signal is constructed by graph modal obtained from the similarity matrix in a temporal interval. Secondly, the signal is optimized by scene transition...
With the fast development of high-speed network and digital video recording technologies, broadcast video has been playing a more and more important role in our daily life. In this paper, we propose a novel news story segmentation scheme which can segment broadcast video into story units with multi-modal information fusion (MMIF) strategy. Compared with traditional methods, the proposed scheme extracts...
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...
Mispronunciation detection is an important component in computer assisted language learning (CALL) system. In this work, we introduce an efficient GLDS-SVM based detection method, which is successfully used in language and speaker identification systems, and combine it with traditional methods. The main ideas include: extended MFCC features with normalized formant trajectory information, and then...
This paper presents an effective method for automatic pronunciation evaluation, which is based on feature extraction and combination. The proposed system extracts different kinds of evaluation features and combines them to produce an ultimate machine score, which predicts the overall pronunciation quality of a student. Experiments on a reading speech database show that most of the selected features...
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