mlsanityData to 7 Oct 2026

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

arXiv 2102.01754 ↗First posted 30 Jan 2021cs.SD cs.AI cs.CLcited by 17

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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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 30 Jan 2021.

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9 of 27 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2018MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversationscited by 636
  4. 2018ESPnet: End-to-End Speech Processing Toolkitcited by 554
  5. 2018Domain Adversarial for Acoustic Emotion Recognitioncited by 25
  6. 2020Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognitioncited by 25
  7. 2018Attention Based Fully Convolutional Network for Speech Emotion Recognitioncited by 23
  8. 2020Recognizing More Emotions with Less Data Using Self-supervised Transfer Learningcited by 7
  9. 2020Multi-Time-Scale Convolution for Emotion Recognition from Speech Audio Signalscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 3; 2024: 4; 2025: 2; 2026: 3.

17 papers.

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  1. 2024Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuningcited by 117
  2. 2021SERAB: A multi-lingual benchmark for speech emotion recognitioncited by 22
  3. 2021EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Modelcited by 18
  4. 2023Vesper: A Compact and Effective Pretrained Model for Speech Emotion Recognitioncited by 16
  5. 2026Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understandingcited by 5
  6. 2023Active Learning Based Fine-Tuning Framework for Speech Emotion Recognitioncited by 2
  7. 2024Facial Expression-Enhanced TTS: Combining Face Representation and Emotion Intensity for Adaptive Speechcited by 2
  8. 2021Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unitcited by 1
  9. 2022Emotional Speech Recognition with Pre-trained Deep Visual Modelscited by 1
  10. 2022Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech datasetcited by 1
  11. 2025Investigating Affect Mining Techniques for Annotation Sample Selection in the Creation of Finnish Affective Speech Corpuscited by 1
  12. 2026HPRO: Hierarchical Progressive Reward Optimization via Preference Extraction for Emotional Text-to-Speechcited by 1
5 more
  1. 2023SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adultscited by 0
  2. 2024Active Learning with Task Adaptation Pre-training for Speech Emotion Recognitioncited by 0
  3. 2024Can We Estimate Purchase Intention Based on Zero-shot Speech Emotion Recognition?cited by 0
  4. 2025Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Rolescited by 0
  5. 2026Watching Movies Like a Human: Egocentric Emotion Understanding for Embodied Companionscited by 0