在一个新的数据集中识别语言:ASMR耳语语音
Meishu Song1,2, Zijiang Yang1,2, Emilia Parada-Cabaleiro3
1Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany.
Frontiers in neuroscience
|July 3, 2023
概括
研究人员创建了ASMR耳语语音 (ASMR-WS) 数据库,以研究自主感官午线响应 (ASMR). 这个新资源有助于开发非发声语言识别系统,用于ASMR相关的刺激.
科学领域:
- 神经学 神经学
- 语言学的语言学.
- 计算机科学 计算机科学
背景情况:
- 自主感官午线反应 (ASMR) 是一种受欢迎的现象,其特点是特定的听觉或视觉刺激引发的刺痛感觉.
- 尽管ASMR很受欢迎,但由于缺乏可访问的开源数据集进行研究,它仍然在很大程度上未被探索.
- 这种差距阻碍了用于分析ASMR相关内容的计算模型的开发.
研究的目的:
- 引入ASMR耳语语音 (ASMR-WS) 数据库,这是一个专门为ASMR研究而设计的新型资源.
- 促进无声语言识别 (无声LID) 系统的开发,能够在ASMR刺激中识别语言.
- 为进一步科学研究ASMR现象提供基础.
主要方法:
- 创建了ASMR-WS数据库,包括38个视频,总计超过10个小时的低语.
- 数据库包括七种语言的录音:中文,英语,法语,意大利语,日语,韩语和西班牙语.
- 基线无声LID模型是使用卷积神经网络 (CNN) 和Mel频率 cepstral系数 (MFCC) 在2秒段上开发的.
主要成果:
- 性能最好的模型在七类无声LID任务中实现了85.74%的未加权平均回忆率和90.83%的准确性.
- 这些结果表明,在ASMR背景下,从低语语中识别语言的可行性.
- 这项研究为未来的研究提供了最初的绩效基准.
结论:
- 现在ASMR-WS数据库已公开提供给研究界,促进对ASMR的进一步研究.
- 未来的研究将探索语音样本持续时间对无声LID系统性能的影响.
- 数据库和基线发现为ASMR研究的不足探索领域做出了重大贡献.
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