使用循环神经网络自动测量语音发声时间和预测.
Yossi Adi1, Joseph Keshet1, Olga Dmitrieva2
1Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel.
概括
本研究引入了一种用于测量语音发声时间 (VOT) 的新算法,包括许多语言中发现的负 VOT. 该算法准确地检测到预测,并测量停止辅音的VOT.
科学领域:
- 语音学和语音科学 语音学和语音科学
- 计算语言学 计算语言学
- 声学语音学的声音学
背景情况:
- 语音发声时间 (VOT) 是一个关键的语音特征,区分停止辅音.
- 现有的自动VOT测量方法主要集中在积极的VOT上,忽略了许多语言中普遍存在的负面VOT.
- 准确测量VOT,包括预测,对于语音分析和语言研究至关重要.
研究的目的:
- 开发和评估一种用于自动测量语音发声时间 (VOT) 的算法,包括正值和负值.
- 准确检测预测事件并测量语音段中单止辅音的VOT.
- 改进自动VOT测量和预测检测的最新技术.
主要方法:
- 在手动标记的语音数据上训练了一个循环神经网络 (RNN).
- RNN模拟了语音信号的动态时间特征,以识别声学事件.
- 该算法处理语音部分以输出爆发开始,爆发持续时间,并预测爆发与信心分数.
主要成果:
- 与现有方法相比,拟议的算法在测量VOT方面表现出优异的性能.
- 该算法在检测预测事件时实现了高精度.
- 该系统有效地测量了积极和负的VOT值.
结论:
- 开发的算法为自动VOT测量提供了强大的解决方案,包括负VOT和预估.
- 这一进步对语音分析和跨语言语音研究有重大影响.
- 基于RNN的方法提供了一种可靠的方法来分析语音信号的时间动态.
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