对比人类和机器在语言分类中使用辅音母音的 nasalization)
Georgia Zellou1, Lila Kim2, Cédric Gendrot2
1Phonetics Lab, Linguistics Department, University of California-Davis, Davis, California 95616, USA.
The Journal of the Acoustical Society of America
|July 16, 2024
概括
人类听众和 wav2vec 2.0 语音模型也类似地使用鼻腔协来识别母音. 虽然两者都在自然的,非鼻音元音上表现更好,但wav2vec 2.0在合成的鼻音元音上表现出色.
科学领域:
- 语音感知研究 语音感知研究
- 计算语言学计算语言学
- 听觉神经科学 听觉神经科学
背景情况:
- 预测性关节,就像鼻化一样,为预测即将到来的语音提供了关键的线索.
- 了解人类和人工系统如何处理这些线索对于语音技术和认知科学至关重要.
研究的目的:
- 为了比较人类听众和自我监督的语音模型 (wav2vec 2.0) 使用鼻腔协articulation用于元音分类的能力.
- 调查母音鼻化和扬声器变异性如何影响人类和模型的分类性能.
主要方法:
- 从60个人类发音者和36个文字转化为语音 (TTS) 声音中利用了鼻化 (CVN) 和非鼻化 (CVC) 的美式英语元音.
- 在不同元音类型和语音源中比较人类听众的分类准确性和 wav2vec 2.0 模型.
主要成果:
- 总体而言,wav2vec 2.0 的性能与人类听众的性能相当.
- 人类和wav2vec 2.0都在自然产生的,非鼻的元音上表现得更好.
- wav2vec 2.0在TTS语音中的鼻音表现优于非鼻音的TTS语音表现.
结论:
- 研究结果表明,人造语音模型可以近似人类处理关节线索.
- 在人类说话者和 wav2vec 2.0 模型中,共同发音使用的变化存在.
- 结果提供了关于语音感知和学习声学特征的共同发音的基础神经机制的见解.
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