# Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning

arXiv: https://arxiv.org/abs/1804.05788 · HTML: https://mlsanity.com/paper/1804.05788/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 21 of 21)

- [Kimi-Audio Technical Report](https://mlsanity.com/paper/2504.18425/) (2025)
- [EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes](https://mlsanity.com/paper/2307.07961/) (2023)
- [Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data](https://mlsanity.com/paper/2212.09171/) (2022)
- [Quantifying and Enhancing Multi-modal Robustness with Modality Preference](https://mlsanity.com/paper/2402.06244/) (2024)
- [Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation](https://mlsanity.com/paper/2108.02510/) (2021)
- [A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition](https://mlsanity.com/paper/2111.02172/) (2021)
- [Bias and Fairness on Multimodal Emotion Detection Algorithms](https://mlsanity.com/paper/2205.08383/) (2022)
- [Interpretability for Multimodal Emotion Recognition using Concept Activation Vectors](https://mlsanity.com/paper/2202.01072/) (2022)
- [Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors](https://mlsanity.com/paper/2203.04443/) (2022)
- [Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models](https://mlsanity.com/paper/2202.08974/) (2022)
- [MMER: Multimodal Multi-task Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2203.16794/) (2022)
- [VoxPrivacy: A Benchmark for Evaluating Interactional Privacy of Speech Language Models](https://mlsanity.com/paper/2601.19956/) (2026)
- [A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2101.00738/) (2021)
- [Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction](https://mlsanity.com/paper/2204.11382/) (2022)
- [Detecting Interlocutor Confusion in Situated Human-Avatar Dialogue: A Pilot Study](https://mlsanity.com/paper/2206.02436/) (2022)
- [Distribution-based Emotion Recognition in Conversation](https://mlsanity.com/paper/2211.04834/) (2022)
- [EmoSpeech: A Corpus of Emotionally Rich and Contextually Detailed Speech Annotations](https://mlsanity.com/paper/2412.06581/) (2024)
- [Eureka-Audio: Triggering Audio Intelligence in Compact Language Models](https://mlsanity.com/paper/2602.13954/) (2026)
- [Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism](https://mlsanity.com/paper/2412.08683/) (2024)
- [ERFC: Happy Customers with Emotion Recognition and Forecasting in Conversation in Call Centers](https://mlsanity.com/paper/2509.18175/) (2025)
- [Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters?](https://mlsanity.com/paper/2607.12787/) (2026)

## Abstract (arXiv)

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. With the advancement of technology our understanding of emotions are advancing, there is a growing need for automatic emotion recognition systems. One of the directions the research is heading is the use of Neural Networks which are adept at estimating complex functions that depend on a large number and diverse source of input data. In this paper we attempt to exploit this effectiveness of Neural networks to enable us to perform multimodal Emotion recognition on IEMOCAP dataset using data from Speech, Text, and Motion capture data from face expressions, rotation and hand movements. Prior research has concentrated on Emotion detection from Speech on the IEMOCAP dataset, but our approach is the first that uses the multiple modes of data offered by IEMOCAP for a more robust and accurate emotion detection.