30,468 research outputs found
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
A Cognitive Science Reasoning in Recognition of Emotions in Audio-Visual Speech
In this report we summarize the state-of-the-art of speech emotion recognition from the signal
processing point of view. On the bases of multi-corporal experiments with machine-learning classifiers, the
observation is made that existing approaches for supervised machine learning lead to database dependent
classifiers which can not be applied for multi-language speech emotion recognition without additional training
because they discriminate the emotion classes following the used training language. As there are experimental
results showing that Humans can perform language independent categorisation, we made a parallel between
machine recognition and the cognitive process and tried to discover the sources of these divergent results. The
analysis suggests that the main difference is that the speech perception allows extraction of language
independent features although language dependent features are incorporated in all levels of the speech signal
and play as a strong discriminative function in human perception. Based on several results in related domains, we
have suggested that in addition, the cognitive process of emotion-recognition is based on categorisation, assisted
by some hierarchical structure of the emotional categories, existing in the cognitive space of all humans. We
propose a strategy for developing language independent machine emotion recognition, related to the
identification of language independent speech features and the use of additional information from visual
(expression) features
Perception of non-verbal emotional listener feedback
This paper reports on a listening test assessing the perception of short non-verbal emotional vocalisations emitted by a listener as feedback to the speaker. We clarify the concepts backchannel and feedback, and investigate the use of affect bursts as a means of giving emotional feedback via the backchannel. Experiments with German and Dutch subjects confirm that the recognition of emotion from affect bursts in a dialogical context is similar to their perception in isolation. We also investigate the acceptability of affect bursts when used as listener feedback. Acceptability appears to be linked to display rules for emotion expression. While many ratings were similar between Dutch and German listeners, a number of clear differences was found, suggesting language-specific affect bursts
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