用频率转换的深复杂卷积变压器对耳植入器接受者进行语音增强.
Nursadul Mamun1, John H L Hansen1
1CRSS: Center for Robust Speech Systems; Cochlear Implant Processing Laboratory (CILab), Department of Electrical and Computer Engineering, University of Texas at Dallas, USA.
概括
本研究介绍了一个深度复杂卷积变压器网络 (DCCTN),通过增强语音大小和阶段来改善耳植入物 (CI) 用户的语音理解. 这种新方法在杂的环境中显著提高了可理解性.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 听觉神经科学 听觉神经科学
背景情况:
- 耳植入物 (CI) 用户在杂的环境中面临沟通挑战,原因是语音信号扭曲.
- 现有的语音增强 (SE) 方法往往忽视了阶段信息在语音感知中的关键作用.
研究的目的:
- 开发一种新的深度学习模型,同时增强语音大小和阶段光谱.
- 在复杂的声学环境中改善CI用户的语音可理解性和语音质量.
主要方法:
- 提出了一个深度复杂卷积变压器网络 (DCCTN),利用一个复杂值的U-Net与瓶中的变压器.
- 该网络包含一个频率转换块,以捕获语音和相关性.
- DCCTN学习了一个复杂的转换矩阵,用于时间频域语音恢复.
主要成果:
- 在客观语音可理解性和质量指标方面,DCCTN的表现优于现有的SE模型 (CRN,DCCRN,GCRN).
- 与CI接受者进行的正式听众评估证实了在噪音条件下显著改善.
- 该模型有效地抑制了非静止噪声,而没有引入音乐工件.
结论:
- 拟议的DCCTN通过解决大小和阶段扭曲,为CI用户提供了一种优越的语音增强方法.
- 这种方法有助于提高听力障碍患者的现实世界沟通.
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