沃兹沃思:一个生成词语数据集,用于比较人类和神经网络中的语音表示
Yunkai Zhu1,2, Cameron Grier3, Amy Garcia3
1School of Communication Sciences and Disorders, University of Central Florida, Orlando, FL, USA. yunkai.zhu@ucf.edu.
Scientific data
|September 26, 2025
概括
研究人员开发了沃兹沃思数据集,以比较语音感知的人工和生物神经网络. 这一新数据集有助于理解人类和机器如何处理口语.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 了解人类语音感知的神经基础对于沟通至关重要.
- 现代神经网络 (NN) 在语音识别方面表现有前途,但它们对人类语音处理的建模尚不清楚.
研究的目的:
- 介绍沃兹沃思数据集,用于比较人工和生物NNs之间的语音表示.
- 促进对语音感知背后的神经机制的研究.
主要方法:
- 合成了1200个标记,用于84个单音节词,控制声学参数 (振幅,持续时间,噪声).
- 进行了人类听力实验,以评估可理解性.
- 利用卷积NN来分析令牌识别能力和错误模式.
主要成果:
- 在人类听力测试中,Wordsworth代币被发现是可以理解的.
- 卷积性NN可以识别沃兹沃思令牌,错误模式部分由声学语音学解释.
- 该数据集为各种研究应用提供了对声学特征的控制操作.
结论:
- 沃兹沃思数据集为比较语音感知的人工和生物神经网络模型提供了有价值的工具.
- 控制合成可以详细调查语音处理中关键的声学音声特征.
- 该数据集支持神经科学研究,使用精确表征的语音令牌.
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