Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users.
Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users.
复制标题
基于神经网络的语音增强可改善人工耳蜗用户的噪声中的语音清晰度。
DOI：
10.1016/j.heares.2016.11.012
复制
发表时间：
2017-02
期刊：
影响因子：
2.8
通讯作者：
Bleeck S
中科院分区：
文献类型：
--
作者：
Goehring T;Bolner F;Monaghan JJ;van Dijk B;Zarowski A;Bleeck S
噪声环境下的语音理解仍然是人工耳蜗（CI）用户在日常生活中面临的主要挑战之一。我们评估了一种基于神经网络（NNSE）的语音增强算法，以提高CI用户在噪声中的语音可懂度。该算法将带噪语音信号分解为时间-频率单元，提取一组受启发的特征并将其馈送到神经网络，以估计哪些频率信道包含更多感知上重要的信息（更高的信噪比，SNR）。该估计用于衰减噪声主导的CI通道并保留用于电刺激的语音主导的CI通道，如在传统的m中的n个CI编码策略中。该算法进行了评估，通过测量语音噪声性能的14个CI用户使用三种类型的背景噪声。比较了两种NNSE算法：一种是在用于测试的目标说话人上训练的说话人相关算法，另一种是在不同说话人上训练的说话人无关算法。在固定和波动的噪声中的语音清晰度显着改善被发现相对于未处理的条件下的扬声器相关的算法在所有的噪声类型和扬声器独立的算法在2出3噪声类型。NNSE算法使用特定于噪声的神经网络，该神经网络推广到相同噪声类型的新片段，并在一系列SNR上工作。该算法有可能提高语音的可懂度在噪声中的CI用户，同时满足低计算复杂度和处理延迟的要求，在CI设备中的应用。评估了一种用于改善人工耳蜗用户在噪声中的语音理解的算法。对于固定和非固定噪声类型，发现了显著的改进。它推广到一个新的扬声器，并在一定范围内的信噪比。小的算法延迟使其适合于实时应用。
Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is used to attenuate noise-dominated and retain speech-dominated CI channels for electrical stimulation, as in traditional n-of-m CI coding strategies. The proposed algorithm was evaluated by measuring the speech-in-noise performance of 14 CI users using three types of background noise. Two NNSE algorithms were compared: a speaker-dependent algorithm, that was trained on the target speaker used for testing, and a speaker-independent algorithm, that was trained on different speakers. Significant improvements in the intelligibility of speech in stationary and fluctuating noises were found relative to the unprocessed condition for the speaker-dependent algorithm in all noise types and for the speaker-independent algorithm in 2 out of 3 noise types. The NNSE algorithms used noise-specific neural networks that generalized to novel segments of the same noise type and worked over a range of SNRs. The proposed algorithm has the potential to improve the intelligibility of speech in noise for CI users while meeting the requirements of low computational complexity and processing delay for application in CI devices. An algorithm for improving speech understanding in noise for cochlear implant users is evaluated. Significant improvements were found for stationary and non-stationary noise types. It generalizes to a novel speaker and works over a range of signal-to-noise ratios. The small algorithmic delay makes it suitable for real-time application.