  In this paper, we propose a novel strategy for text-independent speaker
identification system: Multi-Label Training (MLT). Instead of the commonly used
one-to-one correspondence between the speech and the speaker label, we divide
all the speeches of each speaker into several subgroups, with each subgroup
assigned a different set of labels. During the identification process, a
specific speaker is identified as long as the predicted label is the same as
one of his/her corresponding labels. We found that this method can force the
model to distinguish the data more accurately, and somehow takes advantages of
ensemble learning, while avoiding the significant increase of computation and
storage burden. In the experiments, we found that not only in clean conditions,
but also in noisy conditions with speech enhancement, Multi-Label Training can
still achieve better identification performance than commom methods. It should
be noted that the proposed strategy can be easily applied to almost all current
text-independent speaker identification models to achieve further improvements.
