mlsanityData to 7 Oct 2026

Emotion Recognition In Persian Speech Using Deep Neural Networks

arXiv 2204.13601 ↗First posted 28 Apr 2022cs.SD cs.AI eess.AScited by 7

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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.

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

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5 of 23 references.

References · 5

  1. 2018Attention Based Fully Convolutional Network for Speech Emotion Recognitioncited by 23
  2. 2019ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detectioncited by 21
  3. 2018CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentationcited by 17
  4. 2020Augmenting Generative Adversarial Networks for Speech Emotion Recognitioncited by 11
  5. 2020Cross Lingual Cross Corpus Speech Emotion Recognitioncited by 5

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Citing papers per year: 2022: 2; 2023: 4; 2024: 0; 2025: 1.

7 papers.