如何分析和操纵语音录音中的非线性现象
Andrey Anikin1,2, Christian T Herbst3,4
1Division of Cognitive Science, Lund University, Lund, Sweden.
概括
本综述详细介绍了在语音录制中检测和分析非线性现象 (NLP) 的方法,这些方法对于理解发音至关重要. 它还涵盖操纵NLP以研究听众感知,提供用于声学分析和合成的工具.
科学领域:
- 生物声学是一种生物声学.
- 语音科学 语言科学
- 听觉感知是一种听觉感知.
背景情况:
- 语音产生的非线性现象 (NLP) 对生物通信至关重要.
- 对NLP的手动注释是耗时且不可靠的.
- 客观的声学分析和感知研究需要强大的NLP检测和操纵方法.
研究的目的:
- 对检测和分析声信号中的非线性现象 (NLP) 的方法进行审查和介绍.
- 探索操纵音频录音中的NLP技术,以测试感知效应.
- 为生物声学和语音科学研究人员提供实用工具和算法.
主要方法:
- 利用先进的可视化辅助工具,如重新分配的光谱图和阶段图用于NLP检测.
- 采用客观的声学特征,包括一般描述 (例如,声波与噪声比) 和非线性动态统计 (例如,相关性维度).
- 实施用于操纵NLP的算法,例如添加频率跳跃或亚和声,以及用于复杂变化的参数语音合成.
主要成果:
- 描述了用于量化NLP的客观声学测量,超越了手动注释.
- 在音频中展示了操纵NLP的可行方法,帮助感知研究.
- 提供详细的示例与音频文件和R代码,以促进实际应用.
结论:
- 准确检测和操纵非线性现象 (NLP) 对于推进语音通信研究至关重要.
- 审查的方法和工具为分析发音及其感知影响提供了显著的改进.
- 这项工作支持进一步调查NLP在脊椎动物声调中的机制和沟通功能.
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