  Multi-frame algorithms for single-channel speech enhancement are able to take
advantage from short-time correlations within the speech signal. Deep filtering
(DF) recently demonstrated its capabilities for low-latency scenarios like
hearing aids with its complex multi-frame (MF) filter. Alternatively, the
complex filter can be estimated via an MF minimum variance distortionless
response (MVDR), or MF Wiener filter (WF). Previous studies have shown that
incorporating algorithm domain knowledge using an MVDR filter might be
beneficial compared to the direct filter estimation via DF. In this work, we
compare the usage of various multi-frame filters such as DF, MF-MVDR, or MF-WF
for HAs. We assess different covariance estimation methods for both MF-MVDR and
MF-WF and objectively demonstrate an improved performance compared to direct DF
estimation, significantly outperforming related work while improving the
runtime performance.
