  We introduce a novel all neural model for low-latency directional speech
extraction. The model uses direction of arrival (DOA) embeddings from a
predefined spatial grid, which are transformed and fused into a recurrent
neural network based speech extraction model. This process enables the model to
effectively extract speech from a specified DOA. Unlike previous methods that
relied on hand-crafted directional features, the proposed model trains DOA
embeddings from scratch using speech enhancement loss, making it suitable for
low-latency scenarios. Additionally, it operates at a high frame rate, taking
in DOA with each input frame, which brings in the capability of quickly
adapting to changing scene in highly dynamic real-world scenarios. We provide
extensive evaluation to demonstrate the model's efficacy in directional speech
extraction, robustness to DOA mismatch, and its capability to quickly adapt to
abrupt changes in DOA.
