Multi-modal Dimensional Emotion Recognition using Recurrent Neural Networks

Shizhe Chen · Qin Jin

112 citations0/0 files storedInternational Workshop on Audio/Visual Emotion Challenge (AVEC)

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Emotion and Mood Recognition Speech and Audio Processing Sentiment Analysis and Opinion Mining

Abstract

Emotion recognition has been an active research area with both wide applications and big challenges. This paper presents our effort for the Audio/Visual Emotion Challenge (AVEC2015), whose goal is to explore utilizing audio, visual and physiological signals to continuously predict the value of the emotion dimensions (arousal and valence). Our system applies the Recurrent Neural Networks (RNN) to model temporal information. We explore various aspects to improve the prediction performance including: the dominant modalities for arousal and valence prediction, duration of features, novel loss functions, directions of Long Short Term Memory (LSTM), multi-task learning, different structures for early feature fusion and late fusion. Best settings are chosen according to the performance on the development set. Competitive experimental results compared with the baseline show the effectiveness of the proposed methods.

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References

2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions 2010Survey on speech emotion recognition: Features, classification schemes, and databases 1964Smoothing, Forecasting and Prediction of Discrete Time Series.2011Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge 2000Acoustical properties of speech as indicators of depression and suicidal risk 2010Automatic, Dimensional and Continuous Emotion Recognition 2014Speech Emotion Recognition Using CNN 2013Combining modality specific deep neural networks for emotion recognition in video

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2016Multi-Modal Audio, Video and Physiological Sensor Learning for Continuous Emotion Prediction 2016How deep neural networks can improve emotion recognition on video data 2019Automatic speech emotion recognition using an optimal combination of features based on EMD-TKEO 2018Deep spatio-temporal feature fusion with compact bilinear pooling for multimodal emotion recognition 2020Automatic Emotion Recognition Using Temporal Multimodal Deep Learning

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