轻量级语音增强通过可学习的先前和施罗丁格桥的生成对抗网络)
Zengqiang Shang1, Biao Liu1,2, Mou Wang3
1Laboratory of Speech and Intelligent Information Processing, Institute of Acoustics, Chinese Academy of Sciences, China.
The Journal of the Acoustical Society of America
|December 23, 2025
概括
本研究介绍了一种新的语音增强框架,使用可学习的先验与施罗丁格桥的生成对抗网络. 新方法提高了语音质量和效率,即使在资源有限的环境中.
科学领域:
- 人工智能的人工智能
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 对于语音增强而言,传统的施罗丁格桥梁方法面临着不高效的运输路径,高计算成本和在有限资源的场景中降低质量的挑战.
- 现有模型中的路径交叉导致计算效率低下和性能下降.
研究的目的:
- 开发一种先进的语音增强框架,克服传统的施罗丁格桥梁方法的局限性.
- 提高语音质量,自然性和计算效率,特别是在资源有限的环境中.
主要方法:
- 一个可学习的先前模块与施罗丁格桥的生成对抗网络的集成.
- 利用对抗训练和多尺度损失函数来提高语音质量.
- 开发一个纳米尺寸模型 (0.04M参数) 以实现高效的性能.
主要成果:
- 拟议的框架显著优于语音增强方面的最先进方法.
- 在整体质量 (OVRL),语音质量 (SIG),背景噪音质量 (BAK) 和P808.MOS.等指标上取得了卓越的表现.
- 在资源有限的条件下,即使使用紧的纳米尺寸模型,也表现出强大而有竞争力的结果.
结论:
- 可学习的先前和对抗模型的协同组合有效地提高了语音质量和效率.
- 可学习的前期模块对于最小化路径交叉和优化运输路径至关重要.
- 这种方法为语音增强提供了一个计算高效和高性能解决方案,特别是在边缘计算场景中.
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