自主监督的开放式扬声器识别与拉盖尔-沃罗诺伊描述符
Abu Quwsar Ohi1, Marina L Gavrilova1
1Department of Computer Science, University of Calgary, Calgary, AB T2N1N4, Canada.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究引入了一种新的自我监督方法,用于开放式语音识别,提高行为生物识别的准确性和稳定性. 该方法有效地利用扬声器分布的几何性质来进行增强的验证.
科学领域:
- 行为生物识别技术
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 语音识别是行为生物识别中一个关键但具有挑战性的领域.
- 现有的深度学习方法主要集中在封闭集系统上,使得开放集识别未得到充分探索.
- 在现实场景中进行强大的扬声器验证仍然是一个重大障碍.
研究的目的:
- 提出一个新的自我监督的开放式语音识别框架.
- 通过利用扬声器分布的几何性质来提高扬声器验证的准确性和稳定性.
- 为了解决当前最先进的开放式扬声器识别系统的局限性.
主要方法:
- 开发一个深度神经网络 (DNN),结合一个更广泛的时间语音特征视角.
- 基于Laguerre-Voronoi图表的语音特征提取的实施.
- 使用专门的集群标准训练DNN,该标准只需要正对.
主要成果:
- 与现有的最先进的方法相比,拟议的自主监督系统表现出优越的性能.
- 在开放式扬声器识别准确度中观察到显著的改进.
- 通过实验结果验证了增强的集群表示能力.
结论:
- 拟议的自我监督框架为准确和强大的开放式扬声器识别提供了一个有希望的解决方案.
- 利用扬声器分布的几何性质是提高扬声器验证的有效方法.
- 该方法推进了语音识别中的行为生物识别和机器学习应用领域.
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