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Updated: May 23, 2025

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Eliciting and Analyzing Male Mouse Ultrasonic Vocalization USV Songs
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CoVox:一个对比声调的数据集
Camila Bruder1, Pauline Larrouy-Maestri2,3
1Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, 60322, Frankfurt Am Main, Germany. cajmila@gmail.com.
Behavior research methods
|April 11, 2025
概括
这项研究创建了一个独特的发声数据集,其中歌手以不同的风格表演旋律,并与不同的观众交谈. 该数据集允许在识别发音风格方面具有高准确性,有助于语音研究.
科学领域:
- 语音和听力科学 语言和听力科学
- 声学语音学的声音学
- 音乐心理学 音乐心理学
背景情况:
- 人的声音在唱歌和说话任务中表现出显著的变化.
- 现有的发音数据集往往缺乏生态有效性和风格多样性.
- 开发受控的,但又自然的声音刺激对于语音研究至关重要.
研究的目的:
- 介绍一个新的,生态有效的语音数据集,涵盖各种歌唱和说话风格.
- 为研究声乐风格之间的声学和感知差异提供资源.
- 促进对人类唱歌和说话声音的研究.
主要方法:
- 22位女歌手在三个歌唱风格 (摇篮歌,流行歌,歌剧) 和两个说话风格 (成人,婴儿) 中录制了旋律段落.
- 用巴西葡萄牙语的原创歌词和a/lu/声音制作了刺激.
- 一个强迫选择实验验证实了数据集,听众在风格识别中达到>69%的准确性.
主要成果:
- 创建了一个由1320个发音组成的验证数据集,证明了风格识别的高准确性.
- 声学分析揭示了每个发声风格的独特和对比的声学特征.
- 该数据集为比较语音研究提供了宝贵的资源.
结论:
- 开发的发声数据集在生态上是有效的,在风格上是不同的.
- 本资源支持进一步研究唱歌和说话声音的声学特征和感知.
- 自由可用的录音促进了更广泛的可访问性和声音科学的进步.
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