参数高效的扬声器调整,以增强骨传导的语音
Daniel Fogerty1, Amin Edraki2, Wai-Yip Chan2
1Department of Speech and Hearing Science, University of Illinois Urbana-Champaign, Champaign, Illinois 61820, USA.
JASA express letters
|February 12, 2026
概括
这项研究使用深度神经网络来增强骨传导语音 (BCS),提高看不见的扬声器的可理解性和质量. 个性化技术显著提高BCS性能,即使数据有限.
科学领域:
- 语音处理 语音处理
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 骨传导语音 (BCS) 信号受到有限的频率响应和扬声器扭曲的影响,降低了可理解性.
- 尽管存在质量问题,但BCS在杂环境中提供无噪声通信潜力,因为它与环境噪声隔绝.
研究的目的:
- 研究深度神经网络 (DNN) 模型,以提高BCS质量和可理解性.
- 探索DNN模型的个性化方法,以适应新的,看不见的扬声器,以增强BCS.
- 为了评估完整模型微调与参数效率适应个性化的有效性.
主要方法:
- 专门为BCS增强开发了DNN语音模型.
- 实施并比较了两个个性化策略:完整模型微调和参数有效的调整.
- 通过听众的主观质量评分和客观可理解性/质量指标来评估表现.
主要成果:
- DNN 模型显著提高了 BCS 的可理解性和质量,用于看不见的扬声器.
- 参数效率适应证明了有效的个性化,即使具有有限的扬声器特定数据.
- 主观和客观评估都证实了BCS绩效的大幅改善.
结论:
- 深度神经网络,特别是具有参数有效的适应性,可以有效地增强新发言者的骨导语音.
- 个性化对于优化BCS增强模型至关重要.
- 这种方法有望通过使用BCS技术在高噪音环境中改善通信.
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