โก Quick Summary
This review article evaluates the performance of transformer architectures in speaker-independent Speech Emotion Recognition (SER), highlighting the challenges and advancements in this field. The findings indicate that while most models struggle with accuracies below 40%, combining datasets can enhance performance, achieving up to 58.85% accuracy.
๐ Key Details
- ๐ Focus: Speaker-independent Speech Emotion Recognition (SER)
- ๐งฉ Technology: Transformer architectures
- โ๏ธ Evaluation: Performance benchmarking across multiple datasets
- ๐ Key Performance Metric: Accuracy rates up to 58.85%
๐ Key Takeaways
- ๐ SER is crucial for developing empathetic human-computer interfaces.
- ๐ค Transformers are central to advancements in SER and ASR.
- ๐ Most models achieve accuracies below 40% when trained and tested on different datasets.
- ๐ Combining datasets can significantly improve model performance.
- ๐ Best results achieved with a combined dataset evaluation, reaching 58.85% accuracy.
- ๐ Comprehensive evaluation provides insights into the effectiveness of current SER models.
- ๐ก Generalization of models can be enhanced through diverse data aggregation.
๐ Background
The field of Speech Emotion Recognition (SER) is gaining traction as it plays a vital role in enhancing the interaction between humans and machines. By enabling systems to recognize and respond to human emotions, SER can significantly improve user experience in various applications, from virtual assistants to mental health monitoring. However, achieving speaker-independent recognition remains a challenge, as traditional models often rely heavily on specific training datasets.
๐๏ธ Study
This review article systematically evaluates the performance of various transformer architectures in the context of SER. The authors conducted an independent validation using multiple publicly available datasets, aiming to provide a clear picture of how well these models perform when faced with the challenge of speaker independence. The study emphasizes the importance of rigorous benchmarking in understanding the capabilities and limitations of current SER technologies.
๐ Results
The findings reveal that most transformer models struggle to achieve satisfactory performance, with accuracies often falling below 40% when trained on one dataset and tested on another. However, a notable improvement was observed when models were evaluated using a combination of up to five datasets, resulting in an impressive accuracy of 58.85%. This suggests that leveraging diverse datasets can enhance the generalization capabilities of SER models.
๐ Impact and Implications
The implications of this study are significant for the future of human-computer interaction. By improving the accuracy of SER systems, we can create more responsive and emotionally aware technologies. This advancement could lead to better user experiences in applications such as customer service, mental health support, and interactive entertainment. As we continue to refine these models, the potential for integrating emotional intelligence into machines becomes increasingly feasible.
๐ฎ Conclusion
This review highlights the critical role of transformers in advancing speaker-independent SER, while also acknowledging the challenges that remain. The ability to improve model performance through dataset aggregation is a promising avenue for future research. As we move forward, continued exploration in this field will be essential for developing more sophisticated and empathetic AI systems.
๐ฌ Your comments
What are your thoughts on the advancements in Speech Emotion Recognition? Do you believe that improving emotional intelligence in machines is the future of technology? ๐ฌ Share your insights in the comments below or connect with us on social media:
REVIEW ARTICLE: A Performance Benchmarking Review of Transformers for Speaker-Independent Speech Emotion Recognition.
Abstract
Speech Emotion Recognition (SER) is becoming a key element of speech-based human-computer interfaces, endowing them with some form of empathy towards the emotional status of the human. Transformers have become a central Deep Learning (DL) architecture in natural language processing and signal processing, recently including audio signals for Automatic Speech Recognition (ASR) and SER.ย A central question addressed in this paper is the achievement of speaker-independent SER systems, i.e. systems that perform independently of a specific training set, enabling their deployment in real-world situations by overcoming the typical limitations of laboratory environments. This paper presents a comprehensive performance evaluation review of transformer architectures that have been proposed to deal with the SER task, carrying out an independent validation at different levels over the most relevant publicly available datasets for validation of SER models. The comprehensive experimental design implemented in this paper provides an accurate picture of the performance achieved by current state-of-the-art transformer models in speaker-independent SER.ย We have found that most experimental instances reach accuracies below 40% when a model is trained on a dataset and tested on a different one. A speaker-independent evaluation combining up to five datasets and testing on a different one achieves up to 58.85% accuracy. In conclusion, the SER results improved with the aggregation of datasets, indicating that model generalization can be enhanced by extracting data from diverse datasets.
Author: [‘Portal F’, ‘De Lope J’, ‘Graรฑa M’]
Journal: Int J Neural Syst
Citation: Portal F, et al. REVIEW ARTICLE: A Performance Benchmarking Review of Transformers for Speaker-Independent Speech Emotion Recognition. REVIEW ARTICLE: A Performance Benchmarking Review of Transformers for Speaker-Independent Speech Emotion Recognition. 2025; (unknown volume):2530001. doi: 10.1142/S0129065725300013