Attempts to develop noise-suppression algorithms that can significantly improve speech intelligibility in noise by cochlear implant (CI) users have met with limited success. This is partly because algorithms were sought that would work equally well in all listening situations. Accomplishing this has been quite challenging given the variability in the temporal∕spectral characteristics of real-world maskers. A different approach is taken in the present study focused on the development of environment-specific noise suppression algorithms. The proposed algorithm selects a subset of the envelope amplitudes for stimulation based on the signal-to-noise ratio (SNR) of each channel. Binary classifiers, trained using data collected from a particular noisy environment, are first used to classify the mixture envelopes of each channel as either target-dominated (SNR≥0 dB) or masker-dominated (SNR<0 dB). Only target-dominated channels are subsequently selected for stimulation. Results with CI listeners indicated substantial improvements (by nearly 44 percentage points at 5 dB SNR) in intelligibility with the proposed algorithm when tested with sentences embedded in three real-world maskers. The present study demonstrated that the environment-specific approach to noise reduction has the potential to restore speech intelligibility in noise to a level near to that attained in quiet.
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机译:人工耳蜗植入(CI)用户尝试开发可显着改善噪声中语音清晰度的噪声抑制算法的尝试取得了有限的成功。这部分是因为寻求的算法在所有聆听情况下都同样有效。考虑到现实世界中掩蔽者的时间光谱特性的可变性,实现这一点非常具有挑战性。在本研究中,采用了另一种方法,重点是开发特定于环境的噪声抑制算法。所提出的算法基于每个通道的信噪比(SNR)选择包络幅度的子集进行刺激。使用从特定噪声环境中收集的数据进行训练的二元分类器首先用于将每个通道的混合包络分类为目标控制(SNR≥0dB)或掩蔽控制(SNR <0 dB)。随后仅选择目标主导的通道进行刺激。 CI侦听器的结果表明,在嵌入三个真实世界掩蔽器中的句子进行测试时,所提出算法的清晰度有了显着提高(在5 dB SNR时提高了近44个百分点)。本研究表明,针对环境的降噪方法具有将语音中的语音清晰度恢复到接近安静状态的潜力。
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