对比的扬声器表示学习用硬负采样来识别扬声器识别.
Changhwan Go1, Young Han Lee2, Taewoo Kim2
1Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了一种新的对比学习框架,以提高语音识别的准确性. 该方法有效地减少了与具有挑战性的负样本的相似性,提高了扬声器识别性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 语音处理 语音处理
背景情况:
- 扬声器识别对于安全和认证至关重要,需要提取独特的扬声器功能.
- 目前的方法通常使用分类或对比学习来学习说话者关系.
- 提取高度歧视性特征是实现高扬声器识别率的关键.
研究的目的:
- 使用对比学习开发一个强大的语音识别框架.
- 在扬声器识别培训中尽量减少硬负样本的影响.
- 为了提高语音发言中识别发言者的准确性.
主要方法:
- 使用对比学习进行强大的语音识别的新框架.
- 在对比学习过程中,在小批量中估计硬负样本.
- 采用交叉注意力机制来确定发言对之间的说话协议.
主要成果:
- 拟议的方法在voxceleb1-E数据集上实现了0.98%的等错率 (EER).
- 该框架在voxceleb1-H数据集上产生了1.84%的EER,当在voxceleb2.2上进行训练时.
- 在扬声器识别中,与传统的损失函数相比,表现出优异的性能.
结论:
- 拟议的对比学习框架显著提高了扬声器识别的稳定性.
- 有效地处理硬负样本对于提高扬声器识别精度至关重要.
- 交叉注意力机制有助于通过发言配对可靠的扬声器验证.
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