Politehnica University Timişoara, Department of Automation and Applied Informatics, Timişoara, Romania.
Stud Health Technol Inform. 2024 Aug 22;316:924-928. doi: 10.3233/SHTI240562.
In recent years, artificial intelligence, and machine learning (ML) models have advanced significantly, offering transformative solutions across diverse sectors. Emotion recognition in speech has particularly benefited from ML techniques, revolutionizing its accuracy and applicability. This article proposes a method for emotion detection in Romanian speech analysis by combining two distinct approaches: semantic analysis using GPT Transformer and acoustic analysis using openSMILE. The results showed an accuracy of 74% and a precision of almost 82%. Several system limitations were observed due to the limited and low-quality dataset. However, it also opened a new horizon in our research by analyzing emotions to identify mental health disorders.
近年来，人工智能和机器学习（ML）模型取得了显著进展，为各个领域提供了变革性的解决方案。情感识别在语音方面尤其受益于 ML 技术，使其准确性和适用性得到了极大的提升。本文提出了一种结合两种不同方法的罗马尼亚语语音分析中的情感检测方法：使用 GPT 转换器进行语义分析和使用 openSMILE 进行声学分析。结果显示准确率为 74%，精度接近 82%。由于数据集有限且质量较低，观察到了几个系统限制。然而，通过分析情感来识别心理健康障碍，这也为我们的研究开辟了一个新的前景。
