PROJECT TITLE :
Human emotional expression tends to evolve during a structured manner in the sense that certain emotional evolution patterns, i.e., anger to anger, are a lot of probable than others, e.g., anger to happiness. Furthermore, the perception of an emotional display will be tormented by recent emotional displays. Therefore, the emotional content of past and future observations could provide relevant temporal context when classifying the emotional content of an observation. During this work, we have a tendency to specialise in audio-visual recognition of the emotional content of improvised emotional interactions at the utterance level. We tend to examine context-sensitive schemes for emotion recognition within a multimodal, hierarchical approach: bidirectional Long Short-Term Memory (BLSTM) neural networks, hierarchical Hidden Markov Model classifiers (HMMs), and hybrid HMM/BLSTM classifiers are thought of for modeling emotion evolution inside an utterance and between utterances over the course of a dialog. Overall, our experimental results indicate that incorporating long-term temporal context is beneficial for emotion recognition systems that encounter a selection of emotional manifestations. Context-sensitive approaches outperform those without context for classification tasks like discrimination between valence levels or between clusters within the valence-activation area. The analysis of emotional transitions in our database sheds light into the flow of affective expressions, revealing probably useful patterns.
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