  Streaming models are an essential component of real-time speech enhancement
tools. The streaming regime constrains speech enhancement models to use only a
tiny context of future information. As a result, the low-latency streaming
setup is generally considered a challenging task and has a significant negative
impact on the model's quality. However, the sequential nature of streaming
generation offers a natural possibility for autoregression, that is, utilizing
previous predictions while making current ones. The conventional method for
training autoregressive models is teacher forcing, but its primary drawback
lies in the training-inference mismatch that can lead to a substantial
degradation in quality. In this study, we propose a straightforward yet
effective alternative technique for training autoregressive low-latency speech
enhancement models. We demonstrate that the proposed approach leads to stable
improvement across diverse architectures and training scenarios.
