# ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection

arXiv: https://arxiv.org/abs/1906.01155 · HTML: https://mlsanity.com/paper/1906.01155/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

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

- [emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation](https://mlsanity.com/paper/2312.15185/) (2023)
- [EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark](https://mlsanity.com/paper/2406.07162/) (2024)
- [Meta Learning Text-to-Speech Synthesis in over 7000 Languages](https://mlsanity.com/paper/2406.06403/) (2024)
- [Emotion Recognition In Persian Speech Using Deep Neural Networks](https://mlsanity.com/paper/2204.13601/) (2022)
- [SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion Recognition](https://mlsanity.com/paper/2408.07851/) (2024)
- [Decoding Emotions: A comprehensive Multilingual Study of Speech Models for Speech Emotion Recognition](https://mlsanity.com/paper/2308.08713/) (2023)
- [A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text Corpora](https://mlsanity.com/paper/2211.09956/) (2022)
- [Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition](https://mlsanity.com/paper/2310.00283/) (2023)
- [VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMs](https://mlsanity.com/paper/2603.08936/) (2026)
- [The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains](https://mlsanity.com/paper/2309.12279/) (2023)
- [Utilizing Speech Emotion Recognition and Recommender Systems for Negative Emotion Handling in Therapy Chatbots](https://mlsanity.com/paper/2311.11116/) (2023)
- [Speaker Emotion Recognition: Leveraging Self-Supervised Models for Feature Extraction Using Wav2Vec2 and HuBERT](https://mlsanity.com/paper/2411.02964/) (2024)
- [Speech Emotion Recognition Leveraging OpenAI's Whisper Representations and Attentive Pooling Methods](https://mlsanity.com/paper/2602.06000/) (2026)
- [PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark](https://mlsanity.com/paper/2603.14456/) (2026)
- [Multilingual Emotion Neurons in Large Audio-Language Models](https://mlsanity.com/paper/2608.08772/) (2026)
- [Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition](https://mlsanity.com/paper/2309.12714/) (2023)
- [Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition](https://mlsanity.com/paper/2405.00307/) (2024)
- [A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition](https://mlsanity.com/paper/2410.04633/) (2024)
- [Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across Languages](https://mlsanity.com/paper/2503.21806/) (2025)
- [A Study of ASR Adaptation and Representation Dimensionality Reduction in Persian Speech Emotion Recognition Using Whisper](https://mlsanity.com/paper/2608.05165/) (2026)
- [Neyshekar: An Open Persian Read-Speech Corpus for Automatic Speech Recognition](https://mlsanity.com/paper/2609.14542/) (2026)

## Abstract (arXiv)

This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO). The database includes 3000 semi-natural utterances, equivalent to 3 hours and 25 minutes of speech data extracted from online radio plays. The ShEMO covers speech samples of 87 native-Persian speakers for five basic emotions including anger, fear, happiness, sadness and surprise, as well as neutral state. Twelve annotators label the underlying emotional state of utterances and majority voting is used to decide on the final labels. According to the kappa measure, the inter-annotator agreement is 64% which is interpreted as "substantial agreement". We also present benchmark results based on common classification methods in speech emotion detection task. According to the experiments, support vector machine achieves the best results for both gender-independent (58.2%) and gender-dependent models (female=59.4%, male=57.6%). The ShEMO is available for academic purposes free of charge to provide a baseline for further research on Persian emotional speech.