Emotion recognition using acoustic features and textual content

Chuang, Ze-Jing; Wu, Chung-Hsien

doi:10.1109/icme.2004.1394123

Cited by 16 publications

(1 citation statement)

References 5 publications

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“…To this end, the focus of emotion detection shifted to conversations, specifically ERC. While ERC was solved using heuristics and standard machine learning techniques initially (Fitrianie et al, 2003;Chuang and Wu, 2004;Li et al, 2007), the trend has recently shifted to employing a wide range of deep learning methods (Hazarika et al, 2018a;Zhong et al, 2019a;Li et al, 2020;Ghosal et al, 2019;Jiao et al, 2020;Hazarika et al, 2021;Shen et al, 2020;Poria et al, 2017b;Jiao et al, 2019;Tu et al, 2022;.…”

Section: Related Workmentioning

confidence: 99%

From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues

Kumar,

Akhtar

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC). While considerable research has focused on discerning emotions of individual speakers in monolingual dialogues, understanding the emotional dynamics in codemixed conversations has received relatively less attention. This motivates our undertaking of ERC for code-mixed conversations in this study. Recognizing that emotional intelligence encompasses a comprehension of worldly knowledge, we propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions. To achieve this, we devise an efficient pipeline that extracts relevant commonsense from existing knowledge graphs based on the code-mixed input. Subsequently, we develop an advanced fusion technique that seamlessly combines the acquired commonsense information with the dialogue representation obtained from a dedicated dialogue understanding module. Our comprehensive experimentation showcases the substantial performance improvement obtained through the systematic incorporation of commonsense in ERC. Both quantitative assessments and qualitative analyses further corroborate the validity of our hypothesis, reaffirming the pivotal role of commonsense integration in enhancing ERC.

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