  This paper proposes a low algorithmic latency adaptation of the deep
clustering approach to speaker-independent speech separation. It consists of
three parts: a) the usage of long-short-term-memory (LSTM) networks instead of
their bidirectional variant used in the original work, b) using a short
synthesis window (here 8 ms) required for low-latency operation, and, c) using
a buffer in the beginning of audio mixture to estimate cluster centres
corresponding to constituent speakers which are then utilized to separate
speakers within the rest of the signal. The buffer duration would serve as an
initialization phase after which the system is capable of operating with 8 ms
algorithmic latency. We evaluate our proposed approach on two-speaker mixtures
from the Wall Street Journal (WSJ0) corpus. We observe that the use of LSTM
yields around one dB lower SDR as compared to the baseline bidirectional LSTM
in terms of source to distortion ratio (SDR). Moreover, using an 8 ms synthesis
window instead of 32 ms degrades the separation performance by around 2.1 dB as
compared to the baseline. Finally, we also report separation performance with
different buffer durations noting that separation can be achieved even for
buffer duration as low as 300ms.

Sound;Audio and Speech Processing