# A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text Corpora

arXiv: https://arxiv.org/abs/2211.09956 · HTML: https://mlsanity.com/paper/2211.09956/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Emotion recognition by fusing time synchronous and time asynchronous representations](https://mlsanity.com/paper/2010.14102/) (2020)
- [SERAB: A multi-lingual benchmark for speech emotion recognition](https://mlsanity.com/paper/2110.03414/) (2021)
- [ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection](https://mlsanity.com/paper/1906.01155/) (2019)
- [Fusing ASR Outputs in Joint Training for Speech Emotion Recognition](https://mlsanity.com/paper/2110.15684/) (2021)
- [Emotion Recognition In Persian Speech Using Deep Neural Networks](https://mlsanity.com/paper/2204.13601/) (2022)
- [LSCP: Enhanced Large Scale Colloquial Persian Language Understanding](https://mlsanity.com/paper/2003.06499/) (2020)
- [Is Everything Fine, Grandma? Acoustic and Linguistic Modeling for Robust Elderly Speech Emotion Recognition](https://mlsanity.com/paper/2009.03432/) (2020)
- [Semi-supervised cross-lingual speech emotion recognition](https://mlsanity.com/paper/2207.06767/) (2022)
- [Unsupervised Cross-Lingual Speech Emotion Recognition Using Pseudo Multilabel](https://mlsanity.com/paper/2108.08663/) (2021)

## Built on by (most cited first, 2 of 2)

- [The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains](https://mlsanity.com/paper/2309.12279/) (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 one of the essential perceptual methods of humans in understanding the situation and how to interact with others, therefore, in recent years, it has been tried to add the ability to recognize emotions to human-machine communication systems. Since the SER process relies on labeled data, databases are essential for it. Incomplete, low-quality or defective data may lead to inaccurate predictions. In this paper, we fixed the inconsistencies in Sharif Emotional Speech Database (ShEMO), as a Persian database, by using an Automatic Speech Recognition (ASR) system and investigating the effect of Farsi language models obtained from accessible Persian text corpora. We also introduced a Persian/Farsi ASR-based SER system that uses linguistic features of the ASR outputs and Deep Learning-based models.