关于对语音包裹进行最佳估计的调查,用于两阶段的语音增强
1IDLab, Ghent University-imec, 9000 Gent, Belgium.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究增强了语音外估计,以更好地增强语音. 首斯特拉系数和统一的代码书可以改善语音理解能力下降,反复的神经网络可以提供高效的解决方案.
科学领域:
- 信号处理 信号处理
- 语音技术 语言技术
- 机器学习 机器学习
背景情况:
- 语音增强旨在提高退化音频信号的可理解性和质量.
- 源过器模型将语音分解为激发和信封组件.
- 封面恢复对于增强语音感知至关重要.
研究的目的:
- 调查在退化信号中语音外估计的最佳特征和方法.
- 为实际的语音增强系统开发低复杂度的神经网络架构.
- 评估使用改进的语音包裹的两阶段框架的有效性.
主要方法:
- 使用源过器模型来进行语音信号分解.
- 线性预测系数 (LPCs) 和面系数 (CCs) 的对比,用于信封表示.
- 采用数据驱动的方法:分类 (代码书映射) 和回归 (直接估计).
- 研究了循环神经网络 (RNN),用于将退化的信封映射到代码书条目中.
主要成果:
- 塞普斯特拉系数 (CCs) 在LPCs上显示出优越的包裹代表性.
- 一个统一的语音代码书优于手动分割的语音/沉默代码书.
- 简单的循环神经网络在低复杂度下实现了良好的性能.
- 回归方法在较低的计算成本下提供了更好的性能.
结论:
- 这两个阶段的框架显著改善了语音外的估计和增强.
- 塞普斯特拉系数和统一的代码书是有效的信封表示的关键.
- 低复杂度的神经网络,特别是RNN,适合于实际系统.
- 需要对极端条件的联合语音和噪音估计进行进一步的研究.
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