自动呼吸模式分析从阅读语音信号的自动呼吸模式分析
概括
研究人员从语音信号中提取呼吸模式,绕过不舒服的呼吸带. 这种方法在将呼吸类别从语音分类中获得了79%的准确性,为理解生理状态提供了一种非侵入性的方法.
科学领域:
- 生物医学工程 生物医学工程
- 语音科学 语言科学
- 生理监测 生理监测
背景情况:
- 语音的产生与呼吸过程密切相关.
- 呼吸模式提供了对心理,生理和认知状态的见解.
- 目前捕捉呼吸模式的方法需要昂贵和不舒服的设备,如呼吸带.
研究的目的:
- 开发一种方法,直接从语音信号中提取呼吸模式.
- 调查使用智能手机麦克风进行非侵入性呼吸监测的可行性.
- 识别不同的呼吸模式,并根据语音信号对其进行分类.
主要方法:
- 在100名印度参与者 (20-25岁) 中,同时记录了语音和呼吸信号.
- 参与者阅读了一段语音平衡的英语段落.
- 时间域特征和回归网络被用来从语音中提取呼吸模式.
- 计算建模被用来对呼吸类别进行分类.
主要成果:
- 确定了五种不同的呼吸模式,分为两种广泛的语音呼吸模式.
- 从语音中提取呼吸模式,其中一个类别的皮尔森相关系数为0.70.
- 该研究使用计算建模实现了79%的分类准确度,在使用计算建模区分两个呼吸类别.
结论:
- 呼吸模式可以有效地从语音信号中提取出来,为传统方法提供一种非侵入性的替代方案.
- 语音信号分析具有远程和可访问的生理状态监测的潜力.
- 识别的呼吸类别和提取方法为基于语音的健康评估提供了进一步研究的基础.
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