利用喉仪数据在语音中进行强大的发声检测
Yixuan Zhang1, Heming Wang1, DeLiang Wang1,2
1Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.
The Journal of the Acoustical Society of America
|November 20, 2024
概括
这项研究引入了一种新的监督语音检测模型,使用喉科仪数据. 基于CrossNet的先进模型实现了强大的语音信号分析,超越现有方法,并对新数据集进行概括.
科学领域:
- 语音处理 语音处理
- 机器学习 机器学习
- 生物声学是一种生物声学.
背景情况:
- 准确的发声间隔检测对于语音分析至关重要,包括音调跟踪.
- 现有的方法通常需要对数据集进行特定的参数调整,并且具有有限的概括性.
- 传统的信号处理和深度学习方法在现实世界语音应用中面临挑战.
研究的目的:
- 开发一个强大的监督语音检测模型,用于语音信号.
- 提高发声检测模型的概括能力.
- 为传统方法提供可靠的替代方案,参数调节较少.
主要方法:
- 从CrossNet架构中开发了一个监督语音检测模型.
- 该模型是使用来自喉科仪数据集的参考表达决策来训练的.
- 研究了预训练策略,以提高模型的概括性.
主要成果:
- 拟议的模型展示了强大的语音检测性能.
- 它在准确性方面显著超过了既定的基线方法.
- 该模型显示了对未见的语音数据集的优秀概括性.
结论:
- 开发的监督语音检测模型提供了卓越的性能和通用性.
- 利用喉仪数据和CrossNet架构解决了先前方法的局限性.
- 提供的源代码和数据集将有助于未来的语音信号处理研究.
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