使用深度学习来提高目标说话者在杂的多说话者环境中的可理解性,对于正常听力和听力损失的人来说
Iordanis Thoidis1, Tobias Goehring2
1School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
The Journal of the Acoustical Society of America
|July 31, 2024
概括
一个新的深度学习算法有效地从杂的多语音环境中提取目标语音者的声音. 这项技术显著提高了正常听力和听力受损的听众的语音可理解性.
科学领域:
- 语音处理 语音处理
- 人工智能的人工智能是人工智能.
- 听觉神经科学 听觉神经科学
背景情况:
- 在杂的多音箱环境中,语音理解是很困难的.
- 当前的助听器和语音处理方法在这些复杂的声学环境中扎.
- 现有的技术往往无法在竞争的声音中隔离和增强目标扬声器的声音.
研究的目的:
- 开发和评估一种新型的准因果深度学习算法,用于从杂的多语音组合中提取目标语音者的声音.
- 评估算法的性能,将其推广到各种声条件和扬声器特性.
- 测量算法对有听力损失和没有听力损失的听众语音可理解性的影响.
主要方法:
- 开发一个准因果深度学习算法用于扬声器提取.
- 使用计算指标来评估提取精度的客观评估.
- 在未见的扬声器,不同的扬声器数量,级别和语音体上进行概括测试.
- 用正常听力和听力受损的听众在餐厅噪音中进行双盲句子识别测试.
主要成果:
- 基于扬声器的深度学习算法成功地从杂的多扬声器混合物中提取了目标扬声器.
- 该算法展示了跨不同扬声器,噪音水平和语音数据集的概括能力.
- 观察到语音可理解性的显著改善:正常听力听众的17%和听力受损听众的31%.
结论:
- 基于深度学习的扬声器提取是一种可行的方法,可以在具有挑战性的杂,多扬声器环境中提高语音可理解性.
- 这种方法优于传统的语音增强方法,这些方法缺乏对讲者特定的信息.
- 开发的算法为那些在复杂的听力环境中扎的听力障碍者提供了一个有希望的解决方案.
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