# Emotion Recognition In Persian Speech Using Deep Neural Networks

arXiv: https://arxiv.org/abs/2204.13601 · HTML: https://mlsanity.com/paper/2204.13601/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 5 of 5)

- [Attention Based Fully Convolutional Network for Speech Emotion Recognition](https://mlsanity.com/paper/1806.01506/) (2018)
- [ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection](https://mlsanity.com/paper/1906.01155/) (2019)
- [CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation](https://mlsanity.com/paper/1802.05630/) (2018)
- [Augmenting Generative Adversarial Networks for Speech Emotion Recognition](https://mlsanity.com/paper/2005.08447/) (2020)
- [Cross Lingual Cross Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2003.07996/) (2020)

## Built on by (most cited first, 7 of 7)

- [Decoding Emotions: A comprehensive Multilingual Study of Speech Models for Speech Emotion Recognition](https://mlsanity.com/paper/2308.08713/) (2023)
- [Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approaches](https://mlsanity.com/paper/2211.08029/) (2022)
- [A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text Corpora](https://mlsanity.com/paper/2211.09956/) (2022)
- [From Words and Exercises to Wellness: Farsi Chatbot for Self-Attachment Technique](https://mlsanity.com/paper/2310.09362/) (2023)
- [Utilizing Speech Emotion Recognition and Recommender Systems for Negative Emotion Handling in Therapy Chatbots](https://mlsanity.com/paper/2311.11116/) (2023)
- [Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition](https://mlsanity.com/paper/2309.12714/) (2023)
- [Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Models](https://mlsanity.com/paper/2509.05719/) (2025)

## Abstract (arXiv)

Speech Emotion Recognition (SER) is of great importance in Human-Computer Interaction (HCI), as it provides a deeper understanding of the situation and results in better interaction. In recent years, various machine learning and Deep Learning (DL) algorithms have been developed to improve SER techniques. Recognition of the spoken emotions depends on the type of expression that varies between different languages. In this paper, to further study important factors in the Farsi language, we examine various DL techniques on a Farsi/Persian dataset, Sharif Emotional Speech Database (ShEMO), which was released in 2018. Using signal features in low- and high-level descriptions and different deep neural networks and machine learning techniques, Unweighted Accuracy (UA) of 65.20% and Weighted Accuracy (WA) of 78.29% are achieved.