Learning Behavioural Context

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Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.Comment: 27 pages, 8 figure

Unsupervised learning of generative topic saliency for person re-identification

(c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data

Recognition of nonmanual markers in American Sign Language (ASL) using non-parametric adaptive 2D-3D face tracking

This paper addresses the problem of automatically recognizing linguistically significant nonmanual expressions in American Sign Language from video. We develop a fully automatic system that is able to track facial expressions and head movements, and detect and recognize facial events continuously from video. The main contributions of the proposed framework are the following: (1) We have built a stochastic and adaptive ensemble of face trackers to address factors resulting in lost face track; (2) We combine 2D and 3D deformable face models to warp input frames, thus correcting for any variation in facial appearance resulting from changes in 3D head pose; (3) We use a combination of geometric features and texture features extracted from a canonical frontal representation. The proposed new framework makes it possible to detect grammatically significant nonmanual expressions from continuous signing and to differentiate successfully among linguistically significant expressions that involve subtle differences in appearance. We present results that are based on the use of a dataset containing 330 sentences from videos that were collected and linguistically annotated at Boston University