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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Improving Noise Robust Automatic Speech Recognition with Single-Channel Time-Domain Enhancement Network
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Improving Noise Robust Automatic Speech Recognition with Single-Channel Time-Domain Enhancement Network

机译：用单通道时域增强网络提高噪声鲁棒自动语音识别

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With the advent of deep learning, research on noise-robust automatic speech recognition (ASR) has progressed rapidly. However, ASR performance in noisy conditions of single-channel systems remains unsatisfactory. Indeed, most single-channel speech enhancement (SE) methods (denoising) have brought only limited performance gains over state-of-the-art ASR back-end trained on multi-condition training data. Recently, there has been much research on neural network-based SE methods working in the time-domain showing levels of performance never attained before. However, it has not been established whether the high enhancement performance achieved by such time-domain approaches could be translated into ASR. In this paper, we show that a single-channel time-domain denoising approach can significantly improve ASR performance, providing more than 30 % relative word error reduction over a strong ASR back-end on the real evaluation data of the single-channel track of the CHiME-4 dataset. These positive results demonstrate that single-channel noise reduction can still improve ASR performance, which should open the door to more research in that direction.
机译：随着深度学习的出现，对噪声稳健的自动语音识别（ASR）的研究已经迅速发展。但是，单通道系统嘈杂条​​件下的ASR性能仍然不令人满意。实际上，大多数单通道语音增强（SE）方法（去噪）在多条件训练数据上训练的最先进的ASR后端训练的性能收益有限。最近，在时间域中工作的基于神经网络的SE方法已经有多若干研究，显示了之前从未获得的性能水平。但是，尚未确定是否可以将这种时间域方法达到的高增强性能转换为ASR。在本文中，我们表明，单通道时域去噪方法可以显着提高ASR性能，在单通道轨道的实际评估数据中提供超过30％的相对词误差减少Chime-4数据集。这些阳性结果表明，单通道降噪仍然可以提高ASR性能，这应该在该方向上打开更多的研究。

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