  Real-time speech extraction is an important challenge with various
applications such as speech recognition in a human-like avatar/robot. In this
paper, we propose the real-time extension of a speech extraction method based
on independent low-rank matrix analysis (ILRMA) and rank-constrained spatial
covariance matrix estimation (RCSCME). The RCSCME-based method is a
multichannel blind speech extraction method that demonstrates superior speech
extraction performance in diffuse noise environments. To improve the
performance, we introduce spatial regularization into the ILRMA part of the
RCSCME-based speech extraction and design two regularizers. Speech extraction
experiments demonstrated that the proposed methods can function in real time
and the designed regularizers improve the speech extraction performance.
