Summary of Persian Speech Emotion Recognition by Fine-tuning Transformers, By Minoo Shayaninasab et al.
Persian Speech Emotion Recognition by Fine-Tuning Transformers
by Minoo Shayaninasab, Bagher Babaali
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the application of transformer-based models in Persian speech emotion recognition, a significant but underrepresented area in this domain. The authors fine-tune two models using the shEMO dataset, achieving an accuracy increase from 65% to 80%. To investigate the effect of multilinguality on the fine-tuning process, the models are further fine-tuned using the English IEMOCAP and Persian shEMO datasets, resulting in a boosted accuracy of 82% for the Persian emotion recognition system. This study highlights the importance of transformers in this context and demonstrates their potential to enhance the accuracy of speech emotion recognition systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at recognizing how people feel when they’re talking. Right now, most computer systems can only understand what people are saying, not how they’re feeling. The authors want to change that by using special kinds of computer models called transformers. They tested these models on Persian speech (which is a type of language spoken in some countries) and found that they could get the computers to be much more accurate at recognizing emotions. This is important because it can help us understand people better and even create new tools for communicating. |
Keywords
» Artificial intelligence » Fine tuning » Transformer
