Bimodal emotion recognition

Liyanage C. De Silva · Pei Chi Ng

196 citations0/0 files storedInternational Conference on Automatic Face and Gesture Recognition

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

This paper describes the use of statistical techniques and hidden Markov models (HMM) in the recognition of emotions. The method aims to classify 6 basic emotions (anger, dislike, fear, happiness, sadness and surprise) from both facial expressions (video) and emotional speech (audio). The emotions of 2 human subjects were recorded and analyzed. The findings show that the audio and video information can be combined using a rule-based system to improve the recognition rate.

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2017Communication Without Words

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2006Affect Recognition from Face and Body: Early Fusion vs. Late Fusion 2019Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals 2009Multimodal information fusion application to human emotion recognition from face and speech 2020A survey of speech emotion recognition in natural environment 2011Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data 2005Multimodal approaches for emotion recognition: a survey 2007Bimodal Emotion Recognition using Speech and Physiological Changes 2024A Systematic Review on Multimodal Emotion Recognition: Building Blocks, Current State, Applications, and Challenges

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