  This paper presents a new deep clustering (DC) method called manifold-aware
DC (M-DC) that can enhance hyperspace utilization more effectively than the
original DC. The original DC has a limitation in that a pair of two speakers
has to be embedded having an orthogonal relationship due to its use of the
one-hot vector-based loss function, while our method derives a unique loss
function aimed at maximizing the target angle in the hyperspace based on the
nature of a regular simplex. Our proposed loss imposes a higher penalty than
the original DC when the speaker is assigned incorrectly. The change from DC to
M-DC can be easily achieved by rewriting just one term in the loss function of
DC, without any other modifications to the network architecture or model
parameters. As such, our method has high practicability because it does not
affect the original inference part. The experimental results show that the
proposed method improves the performances of the original DC and its expansion
method.
