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Exponential growth of media consumption in online social networks demands effective recommendation to improve the quality of experience especially for on-the-go mobile users. By means of large-scale trace-driven measurements over mobile Twitter traces from users, we reveal the significance of affective features in shaping users’ social media behaviors. Existing recommender systems however, rarely...
Social media is rocking the world in recent year, which makes modeling social media contents important. However, the heterogeneity of social media data is the main constraint. This paper focuses on inferring emotions from large-scale social media data. Tweets on social media platform, always containing heterogeneous information from different combinations of modalities, are utilized to construct a...
Speech are widely used to express one's emotion, intention, desire, etc. in social network communication, deriving abundant of internet speech data with different speaking styles. Such data provides a good resource for social multimedia research. However, regarding different styles are mixed together in the internet speech data, how to classify such data remains a challenging problem. In previous...
This paper investigates the incorporation of hidden Markov model (HMM) based emphatic speech synthesis for audio exaggeration into an audio-visual speech synthesis framework for the corrective feedback in computer-aided pronunciation training (CAPT). To improve the voice quality of the synthetic emphatic speech, this paper proposes a new method for HMM training. In this method, the contextual questions...
This paper presents a hybrid model which combines conditional random fields (CRFs) with dynamic gazetteers (DGs) for the task of Chinese named entity recognition (NER). In the previous work of NER, gazetteers were widely used. But their gazetteers were all static ones which cannot adapt themselves to the new domains and new out-of-vocabulary named entities (OOVNEs). In this work, we build and maintain...
In concatenative based speech synthesis, the purpose of unit selection is to select proper speech units from speech corpus by measuring how well the selected units match the given features. Perceptual test indicates that some features are always preferred to make perceptual distinction between units. Such features should be judged prior to others in unit selection. In this work, we attempt to identify...
Computer-aided Pronunciation Training (CAPT) technologies enable the use of automatic speech recognition to detect mispronunciations in second language (L2) learners' speech. In order to further facilitate learning, we aim to be able to develop a principle-based method for generating a gradation of the severity of mispronunciations. This paper presents an approach towards gradation that is motivated...
Probabilistic neural network (PNN), a kind of radial basis networks, is usually used for classification problems. It has the advantages of much faster training process and more accurate results using the minimum Bayesian risk criterion compared with other neural networks. In this paper, we use this neural network in brain-machine interface for decoding neural ensemble activity. Rats were trained to...
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