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Studies have shown that ranking emotional attributes through preference learning methods has significant advantages over conventional emotional classification/regression frameworks. Preference learning is particularly appealing for retrieval tasks, where the goal is to identify speech conveying target emotional behaviors (e.g., positive samples with low arousal). With recent advances in deep neural...
A speech emotion retrieval system aims to detect a subset of data with specific expressive content. Preference learning represents an appealing framework to rank speech samples in terms of continuous attributes such as arousal and valence. The training of ranking classifiers usually requires pairwise samples where one is preferred over the other according to a specific criterion. For emotional databases,...
A common approach to recognize emotion from speech is to estimate multiple acoustic features at sentence or turn level. These features are derived independent of the underlying lexical content. Studies have demonstrated that lexical dependent models improve emotion recognition accuracy. However, current practical approaches can only model small lexical units like phonemes, syllables or few key words,...
Enhancing the wear ability and reducing the form factor often are among the major objectives in design of wearable platforms. Power optimization techniques will significantly reduce the form factor and/or will prolong the time intervals between recharges. In this paper, we propose an ultra low power programmable architecture based on Dynamic Time Warping specifically designed for wearable inertial...
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