在DistilHuBERT中进行特征和分类器级域调整,用于跨体语音情感识别
Niloufar Naeeni1, Babak Nasersharif1
1Computer Engineering Department, K. N. Toosi University of Technology, Shariati Ave., Tehran, Iran.
Computers in biology and medicine
|June 7, 2025
概括
这项研究使用DistilHuBERT和域调整增强了跨体语音情感识别. 最好的方法获得了92.01%的准确性,改善了跨不同数据集的情绪识别.
科学领域:
- 语音处理和机器学习
- 人工智能在情感计算中的应用
背景情况:
- 跨体语音情感识别 (CCSER) 面临挑战,因为数据集的变化,如性别和语言.
- 需要强大的模型来准确地识别不同数据集的发言人情绪.
研究的目的:
- 通过使用自我监督的语音表示来为CCSER开发有效的域调整策略.
- 提高不同语音数据集的情绪识别模型的准确性和稳定性.
主要方法:
- 使用DistilHuBERT进行自我监督的语音表示.
- 提出了四种特征级域调整 (FDA) 方法,包括语网络和微调的DistilHuBERT层.
- 通过更新分类器以目标数据集部分来实现分类器级域调整 (CDA).
主要成果:
- 第四种FDA方法与CDA相结合,产生了最高的准确性.
- 在使用ShEMO作为源数据集的EMODB数据集上实现了92.01%的准确性.
- 在CCSER中证明了拟议的域调整技术的有效性.
结论:
- 拟议的FDA和CDA方法显著提高了CCSER的性能.
- 将DistilHuBERT (第四种FDA方法) 的CNN和变压器层与CDA相结合的微调是一种有前途的方法,用于强大的情感识别.
- 这项研究有助于在各种语音数据中建立更准确,更可靠的情感识别系统.
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