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Multi-objective learning based speech enhancement method to increase speech quality and intelligibility for hearing aid device users

机译:基于多目标学习的语音增强方法,为助听器设备用户提高语音质量和清晰度

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摘要

Background noise is a critical issue for hearing aid device users; a common solution to address this problem is speech enhancement (SE). In recent times, a novel SE approach based on deep learning technology, called deep denoising autoencoder (DDAE), has been proposed. Previous studies show that the DDAE SE approach provides superior noise suppression capabilities and produces less distortion than any of the classical SE approaches in the case of processed speech. Motivated by the improved results using DDAE shown in previous studies, we propose the multi-objective learning-based DDAE (M-DDAE) SE approach in this study; in addition, we evaluated its speech quality and intelligibility improvements using seven typical hearing loss audiograms. The experimental results of our objective evaluations show that our M-DDAE approach achieved significantly better results than the DDAE approach in most test conditions. Considering this, the proposed M-DDAE SE approach can be potentially used to further improve the listening performance of hearing aid devices in noisy conditions. (C) 2018 Elsevier Ltd. All rights reserved.
机译:背景噪声对于助听器设备用户来说是一个关键问题。解决此问题的常见解决方案是语音增强(SE)。近年来,已经提出了一种基于深度学习技术的新型SE方法,称为深度降噪自动编码器(DDAE)。先前的研究表明,在处理语音的情况下,DDAE SE方法具有出色的噪声抑制能力,并且比任何经典SE方法产生的失真都小。出于先前研究中显示的使用DDAE改善结果的动机,我们在本研究中提出了基于多目标学习的DDAE(M-DDAE)SE方法。此外,我们使用七个典型的听力损失听力图评估了其语音质量和清晰度。我们的客观评估的实验结果表明,在大多数测试条件下,我们的M-DDAE方法比DDAE方法取得了明显更好的结果。考虑到这一点,建议的M-DDAE SE方法可潜在地用于进一步改善嘈杂条件下助听器的收听性能。 (C)2018 Elsevier Ltd.保留所有权利。

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