190 resultados para emotional speech


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This paper reports on the findings from a longitudinal survey of the drug use behaviours of young people who were attending Emotional and Behavioural Difficulty (EBD) units from the age of 11-16 years. It forms part of the Belfast Youth Development Study, a longitudinal study of adolescent drug use. This paper presents a follow-up report to a cross-sectional paper that reported on drug use behaviours of a sample of young people attending EBD units when aged 12/13 years at school year 9 (McCrystal et al 2005a). In the present paper reported drug use and behaviours associated with increased risk of its use between the ages of 11-16 years were examined. The findings show that those attending EBD Units consistently reported higher levels of licit and illicit drug use throughout adolescence. Compared with young people in mainstream school, higher levels of behaviours associated with drug use including antisocial behaviour, disaffection with school, and poor communication with their parents/guardians were noted. These findings have implications for the development and timing of targeted prevention initiatives for young people attending EBD units at all stages of adolescent development.

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We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.

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In this paper we present the application of Hidden Conditional Random Fields (HCRFs) to modelling speech for visual speech recognition. HCRFs may be easily adapted to model long range dependencies across an observation sequence. As a result visual word recognition performance can be improved as the model is able to take more of a contextual approach to generating state sequences. Results are presented from a speaker-dependent, isolated digit, visual speech recognition task using comparisons with a baseline HMM system. We firstly illustrate that word recognition rates on clean video using HCRFs can be improved by increasing the number of past and future observations being taken into account by each state. Secondly we compare model performances using various levels of video compression on the test set. As far as we are aware this is the first attempted use of HCRFs for visual speech recognition.