End-to-end models for robust automatic speech recognition (ASR) have not been sufficiently well-explored in prior work. With end-to-end models, one could choose to preprocess the input speech using speech enhancement techniques and train the model using enhanced speech. Another alternative is to pass the noisy speech as input and modify the model architecture to adapt to noisy speech. A systematic comparison of these two approaches for end-to-end robust ASR has not been attempted before. We address this gap and present a detailed comparison of speech enhancement-based techniques and three different model-based adaptation techniques covering data augmentation, multi-task learning, and adversarial learning for robust ASR. While adversarial learning is the best-performing technique on certain noise types, it comes at the cost of degrading clean speech WER. On other relatively stationary noise types, a new speech enhancement technique outperformed all the model-based adaptation techniques. This suggests that knowledge of the underlying noise type can meaningfully inform the choice of adaptation technique.

翻译：在先前的工作中,尚未充分很好地探索稳健自动语音识别(ASR)的端对端模式。在端对端模型中,人们可以选择使用增强语音技术预处理输入式演讲,并用强化语音培训模型。另一个替代办法是将吵闹的演讲作为输入,并修改模型结构以适应吵闹的演讲。以前没有尝试过对端对端稳健自动语音识别(ASR)这两种方法进行系统比较。我们解决了这一差距,并详细比较了基于语音强化的技术和三种基于模型的不同适应技术,包括数据增强、多任务学习和强健的ASR对抗性学习。虽然对抗性学习是某些噪音类型的最佳技术,但代价是有辱人格的清洁语音WER。关于其他相对固定的噪音类型,一种新的增强语音技术超越了所有基于模型的适应技术。这表明,对基本噪音类型的知识可以为适应技术的选择提供有意义的信息。