# Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues

arXiv: https://arxiv.org/abs/2502.10973 · HTML: https://mlsanity.com/paper/2502.10973/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 50 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://mlsanity.com/paper/1708.04299/) (2017)
- [MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2107.06779/) (2021)
- [Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations](https://mlsanity.com/paper/1909.10681/) (2019)
- [EmotionLines: An Emotion Corpus of Multi-Party Conversations](https://mlsanity.com/paper/1802.08379/) (2018)
- [DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition](https://mlsanity.com/paper/2012.08695/) (2020)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database](https://mlsanity.com/paper/2205.10237/) (2022)
- [M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2206.02187/) (2022)
- [HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition](https://mlsanity.com/paper/1904.04446/) (2019)
- [Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twi](https://mlsanity.com/paper/1912.02481/) (2019)
- [Multimodal Multi-loss Fusion Network for Sentiment Analysis](https://mlsanity.com/paper/2308.00264/) (2023)
- [Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances](https://mlsanity.com/paper/2407.21315/) (2024)
- [BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks](https://mlsanity.com/paper/2407.04279/) (2024)

## Built on by (most cited first, 6 of 6)

- [Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects](https://mlsanity.com/paper/2505.20511/) (2025)
- [OmniVox: Zero-Shot Emotion Recognition with Omni-LLMs](https://mlsanity.com/paper/2503.21480/) (2025)
- [Multi-Party Conversational Agents: A Survey](https://mlsanity.com/paper/2505.18845/) (2025)
- [Detecting Mental Manipulation in Speech via Synthetic Multi-Speaker Dialogue](https://mlsanity.com/paper/2601.08342/) (2026)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)
- [LongEmo: Towards Emotion Understanding and Reasoning in Long Videos](https://mlsanity.com/paper/2609.40079/) (2026)

## Abstract (arXiv)

In this paper, we introduce the Akan Conversation Emotion (ACE) dataset, the first multimodal emotion dialogue dataset for an African language, addressing the significant lack of resources for low-resource languages in emotion recognition research. ACE, developed for the Akan language, contains 385 emotion-labeled dialogues and 6,162 utterances across audio, visual, and textual modalities, along with word-level prosodic prominence annotations. The presence of prosodic labels in this dataset also makes it the first prosodically annotated African language dataset. We demonstrate the quality and utility of ACE through experiments using state-of-the-art emotion recognition methods, establishing solid baselines for future research. We hope ACE inspires further work on inclusive, linguistically and culturally diverse NLP resources.