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Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc.
Emotion recognition in conversations is crucial for building empathetic machines.
#2 best model for Emotion Recognition in Conversation on IEMOCAP
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos.
The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.
Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers.
Emotion recognition has become an important field of research in Human Computer Interactions as we improve upon the techniques for modelling the various aspects of behaviour.
In this work, we adopt a feature-engineering based approach to tackle the task of speech emotion recognition.
SOTA for Speech Emotion Recognition on IEMOCAP (F1 metric )
In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system.