对变压器的性能基准评价,用于扬声器独立的语音情感识别
Francisco Portal1, Javier De Lope1, Manuel Graña2
1Department of Artificial Intelligence, Universidad Politécnica de Madrid, Madrid, Spain.
International journal of neural systems
|July 29, 2025
概括
语音情感识别 (SER) 的变压器模型显示,当在各种数据集上进行训练时,可以提高概括性. 聚合数据可以提高语音独立的SER系统性能,这对于真实世界的人与计算机的互动至关重要.
科学领域:
- 人工智能的人工智能
- 语音处理 语音处理
- 机器学习 机器学习
背景情况:
- 语音情感识别 (SER) 对于同情的人机界面至关重要.
- 变压器架构越来越多地用于音频信号处理,包括SER.
- 实现扬声器独立的SER对于现实应用至关重要.
研究的目的:
- 为了全面评估变压器架构的扬声器独立的SER.
- 在多个公共数据集中验证SER模型的性能.
- 识别影响SER系统概括性的因素.
主要方法:
- 独立验证为SER提出的变压器架构.
- 对最相关的公开可用的 SER 数据集进行绩效评估.
- 通过交叉数据集测试分析语音独立的SER性能.
主要成果:
- 大多数变压器模型在交叉数据集评估中准确度低于40%.
- 在将多达五个数据集结合时,扬声器独立的SER性能提高到58.85%的准确性.
- 聚合不同的数据集显著增强了模型的概括性.
结论:
- 变压器模型对SER有希望,但通用化仍然是一个挑战.
- 数据集的聚合是提高语音独立的 SER 性能的关键.
- 在现实世界中,多种数据源对于强大的SER系统至关重要.
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