Improving noise robust automatic speech recognition with single-channel time-domain enhancement network
This addresses the challenge of unsatisfactory ASR performance in noisy conditions for single-channel systems, though it is incremental as it builds on existing time-domain enhancement methods.
The paper tackled the problem of improving noise-robust automatic speech recognition (ASR) in single-channel systems by using a time-domain denoising approach, achieving more than 30% relative word error reduction on the CHiME-4 dataset.
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.