  We propose a block-online algorithm of guided source separation (GSS). GSS is
a speech separation method that uses diarization information to update
parameters of the generative model of observation signals. Previous studies
have shown that GSS performs well in multi-talker scenarios. However, it
requires a large amount of calculation time, which is an obstacle to the
deployment of online applications. It is also a problem that the offline GSS is
an utterance-wise algorithm so that it produces latency according to the length
of the utterance. With the proposed algorithm, block-wise input samples and
corresponding time annotations are concatenated with those in the preceding
context and used to update the parameters. Using the context enables the
algorithm to estimate time-frequency masks accurately only from one iteration
of optimization for each block, and its latency does not depend on the
utterance length but predetermined block length. It also reduces calculation
cost by updating only the parameters of active speakers in each block and its
context. Evaluation on the CHiME-6 corpus and a meeting corpus showed that the
proposed algorithm achieved almost the same performance as the conventional
offline GSS algorithm but with 32x faster calculation, which is sufficient for
real-time applications.
