# Interpretability for Multimodal Emotion Recognition using Concept Activation Vectors

arXiv: https://arxiv.org/abs/2202.01072 · HTML: https://mlsanity.com/paper/2202.01072/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 18 of 18)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues](https://mlsanity.com/paper/1911.05659/) (2019)
- [Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling](https://mlsanity.com/paper/1806.06228/) (2018)
- [On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors](https://mlsanity.com/paper/2005.02000/) (2020)
- [SPICE: Self-supervised Pitch Estimation](https://mlsanity.com/paper/1910.11664/) (2019)
- [Hybrid Curriculum Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2112.11718/) (2021)
- [Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning](https://mlsanity.com/paper/1804.05788/) (2018)
- [Robust Semantic Interpretability: Revisiting Concept Activation Vectors](https://mlsanity.com/paper/2104.02768/) (2021)
- [Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition](https://mlsanity.com/paper/2201.06309/) (2022)
- [Explaining Deep Neural Networks using Unsupervised Clustering](https://mlsanity.com/paper/2007.07477/) (2020)
- [Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity](https://mlsanity.com/paper/2107.10790/) (2021)

## Built on by (most cited first, 8 of 8)

- [Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions](https://mlsanity.com/paper/2306.05731/) (2023)
- [Concept-Based Techniques for "Musicologist-friendly" Explanations in a Deep Music Classifier](https://mlsanity.com/paper/2208.12485/) (2022)
- [A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future](https://mlsanity.com/paper/2412.14056/) (2024)
- [Learning Unsupervised Hierarchies of Audio Concepts](https://mlsanity.com/paper/2207.11231/) (2022)
- [Towards Interpretability in Audio and Visual Affective Machine Learning: A Review](https://mlsanity.com/paper/2306.08933/) (2023)
- [VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition](https://mlsanity.com/paper/2208.11450/) (2022)
- [Unsupervised discovery of Interpretable Visual Concepts](https://mlsanity.com/paper/2309.00018/) (2023)
- [Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts](https://mlsanity.com/paper/2310.11450/) (2023)

## Abstract (arXiv)

Multimodal Emotion Recognition refers to the classification of input video sequences into emotion labels based on multiple input modalities (usually video, audio and text). In recent years, Deep Neural networks have shown remarkable performance in recognizing human emotions, and are on par with human-level performance on this task. Despite the recent advancements in this field, emotion recognition systems are yet to be accepted for real world setups due to the obscure nature of their reasoning and decision-making process. Most of the research in this field deals with novel architectures to improve the performance for this task, with a few attempts at providing explanations for these models' decisions. In this paper, we address the issue of interpretability for neural networks in the context of emotion recognition using Concept Activation Vectors (CAVs). To analyse the model's latent space, we define human-understandable concepts specific to Emotion AI and map them to the widely-used IEMOCAP multimodal database. We then evaluate the influence of our proposed concepts at multiple layers of the Bi-directional Contextual LSTM (BC-LSTM) network to show that the reasoning process of neural networks for emotion recognition can be represented using human-understandable concepts. Finally, we perform hypothesis testing on our proposed concepts to show that they are significant for interpretability of this task.