语音情感识别 纳入相对难度和标记可靠性
Youngdo Ahn1, Sangwook Han1, Seonggyu Lee1
1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-gu, Gwangju 61005, Republic of Korea.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
这项研究引入了一种新的语音情感识别 (SER) 方法,使用自定义损失函数和标签光滑. 该方法通过专注于样本难度和标签可靠性,在多样化,未见的数据集上增强SER模型的稳定性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 语音处理 语音处理
背景情况:
- 语音情感识别 (SER) 模型由于不同的情感表达而经常与未见的数据作斗争.
- 现有的方法,如规范化或指标损失,旨在提高SER的稳定性.
- 挑战来自情感上下文和影响培训数据的注释器可靠性.
研究的目的:
- 开发一种语音情感识别 (SER) 方法,该方法对未见的 corpora. robust.
- 将训练样本难度和标签可靠性纳入SER模型训练.
- 在培训分布之外,在各种数据集上提高SER性能.
主要方法:
- 提出了一种新的损失函数,灵感来自Proxy-Anchor损失,优先考虑难以分类的样本.
- 通过预先训练的SER模型对错误分类的样品引入标签光滑,以减轻不可靠的标签.
- 将相对样本难度和标签可靠性整合到培训过程中.
主要成果:
- 拟议的SER方法在看不见的实体上表现得更好.
- 新的损失函数通过专注于具有挑战性的样本来有效地指导模型.
- 对错误分类数据的标签平滑进一步增强了模型的概括能力.
结论:
- 拟议的SER方法增强了对未见的语音情感数据变化的模型稳定性.
- 纳入样本难度和标签可靠性对于有效的SER至关重要.
- 新型损失函数和标签平滑的结合为未来的SER研究提供了有希望的方向.
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