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Fusion of Acoustic and Linguistic Information using Supervised Autoencoder for Improved Emotion Recognition by Bogdan Vlasenko; RaviShankar Prasad; Mathew Magimai.-Doss is a scholarly article available to read on EtoBox.

What is Fusion of Acoustic and Linguistic Information using Supervised Autoencoder for Improved Emotion Recognition about?

Automatic recognition of human emotion has a wide range of applications and has always attracted increasing attention. Expressions of human emotions can apparently be identified across different modalities of communication, such as speech, text, mimics, etc. The 'Multimodal Sentiment Analysis in Real-life Media' (MuSe) 2021 challenge provides an environment to develop new techniques to recognize human emotions or sentiments using multiple modalities (audio, video, and text) over in-the-wild data. The challenge encourages to jointly model the information across audio, video and text modalities, for improving emotion recognition. The present paper describes our attempt towards the MuSe-Sent task in the challenge. The goal of the sub-challenge is to perform turn-level prediction of emotions within the arousal and valence dimensions. In the paper, we investigate different approaches to optimally fuse linguistic and acoustic information for emotion recognition systems. The proposed systems employ features derived from these modalities, and uses different deep learning architectures to explore their cross-dependencies. Wide range of acoustic and linguistic features provided by organizers

Author

Bogdan Vlasenko; RaviShankar Prasad; Mathew Magimai.-Doss

Publisher

ACM

Published

2021

Language

EN