一台机器能区分高低的社会声吗?
Anne-Maria Laukkanen1, Sudarsana Reddy Kadiri2, Shrikanth Narayanan2
1Speech and Voice Research Laboratory, Tampere University, Tampere, Finland.
Journal of voice : official journal of the Voice Foundation
|October 25, 2024
概括
机器学习模型现在可以自动区分语言中较低和较高的社交声. 这项技术为研究语言模式的研究人员提供了更有效的替代手动分析.
科学领域:
- 社会语言学 社会语言学
- 语音科学 语言科学
- 计算语言学 计算语言学
- 语音研究 语音研究
背景情况:
- 社会,一种声乐特征,越来越普遍,特别是在女演讲者中.
- 之前的研究依赖于主观感知评估和有限的声学参数 (例如,HNR,CPP) 来研究社会声.
- 需要自动化分析方法来克服手工声评估的劳动密集性.
研究的目的:
- 开发和评估机器学习 (ML) 模型,自动区分具有较低和较高社会声量的语音.
- 建立基准ML分类系统,用于未来的社会探测研究.
主要方法:
- 两个语音专家对90名女性芬兰语说者的连续语音样本进行了感知评估,以分类声水平 (低与高).
- 七个不同的ML模型被训练使用语音信号和相应的 creak标签.
- 作为模型训练的特征,使用了三个光谱表示 (mel-spectrogram,mel-frequency cepstral系数和其他).
主要成果:
- 获得的最高分类准确率为71.1%.
- 两个系统表现出最佳性能:一个使用mel-spectrogram特征的Adaboost分类器和一个使用mel-frequency cepstral系数特征的决策树分类器.
结论:
- 机器学习技术可以有效地帮助研究人员在不断增长的社会分析领域.
- 开发的ML分类系统为未来的研究提供了有价值的基线性能指标.
- 使用机器学习的自动化分析提供了一个更有效和可扩展的方法,与传统的手动方法相比,用于评估社会.
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