# LSSED: a large-scale dataset and benchmark for speech emotion recognition

arXiv: https://arxiv.org/abs/2102.01754 · HTML: https://mlsanity.com/paper/2102.01754/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 30 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Domain Adversarial for Acoustic Emotion Recognition](https://mlsanity.com/paper/1804.07690/) (2018)
- [Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition](https://mlsanity.com/paper/2006.11538/) (2020)
- [Attention Based Fully Convolutional Network for Speech Emotion Recognition](https://mlsanity.com/paper/1806.01506/) (2018)
- [Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning](https://mlsanity.com/paper/2011.05585/) (2020)
- [Multi-Time-Scale Convolution for Emotion Recognition from Speech Audio Signals](https://mlsanity.com/paper/2003.03375/) (2020)

## Built on by (most cited first, 17 of 17)

- [Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning](https://mlsanity.com/paper/2406.11161/) (2024)
- [SERAB: A multi-lingual benchmark for speech emotion recognition](https://mlsanity.com/paper/2110.03414/) (2021)
- [EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model](https://mlsanity.com/paper/2106.09317/) (2021)
- [Vesper: A Compact and Effective Pretrained Model for Speech Emotion Recognition](https://mlsanity.com/paper/2307.10757/) (2023)
- [Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding](https://mlsanity.com/paper/2601.16449/) (2026)
- [Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition](https://mlsanity.com/paper/2310.00283/) (2023)
- [Facial Expression-Enhanced TTS: Combining Face Representation and Emotion Intensity for Adaptive Speech](https://mlsanity.com/paper/2409.16203/) (2024)
- [Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit](https://mlsanity.com/paper/2106.09539/) (2021)
- [Emotional Speech Recognition with Pre-trained Deep Visual Models](https://mlsanity.com/paper/2204.03561/) (2022)
- [Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset](https://mlsanity.com/paper/2211.07307/) (2022)
- [Investigating Affect Mining Techniques for Annotation Sample Selection in the Creation of Finnish Affective Speech Corpus](https://mlsanity.com/paper/2505.17833/) (2025)
- [HPRO: Hierarchical Progressive Reward Optimization via Preference Extraction for Emotional Text-to-Speech](https://mlsanity.com/paper/2606.28249/) (2026)
- [SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults](https://mlsanity.com/paper/2311.14483/) (2023)
- [Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition](https://mlsanity.com/paper/2405.00307/) (2024)
- [Can We Estimate Purchase Intention Based on Zero-shot Speech Emotion Recognition?](https://mlsanity.com/paper/2410.09636/) (2024)
- [Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Roles](https://mlsanity.com/paper/2510.03060/) (2025)
- [Watching Movies Like a Human: Egocentric Emotion Understanding for Embodied Companions](https://mlsanity.com/paper/2604.15823/) (2026)

## Abstract (arXiv)

Speech emotion recognition is a vital contributor to the next generation of human-computer interaction (HCI). However, current existing small-scale databases have limited the development of related research. In this paper, we present LSSED, a challenging large-scale english speech emotion dataset, which has data collected from 820 subjects to simulate real-world distribution. In addition, we release some pre-trained models based on LSSED, which can not only promote the development of speech emotion recognition, but can also be transferred to related downstream tasks such as mental health analysis where data is extremely difficult to collect. Finally, our experiments show the necessity of large-scale datasets and the effectiveness of pre-trained models. The dateset will be released on https://github.com/tobefans/LSSED.