  Most studies on speaker verification systems focus on long-duration
utterances, which are composed of sufficient phonetic information. However, the
performances of these systems are known to degrade when short-duration
utterances are inputted due to the lack of phonetic information as compared to
the long utterances. In this paper, we propose a method that compensates for
the performance degradation of speaker verification for short utterances,
referred to as "segment aggregation". The proposed method adopts an
ensemble-based design to improve the stability and accuracy of speaker
verification systems. The proposed method segments an input utterance into
several short utterances and then aggregates the segment embeddings extracted
from the segmented inputs to compose a speaker embedding. Then, this method
simultaneously trains the segment embeddings and the aggregated speaker
embedding. In addition, we also modified the teacher-student learning method
for the proposed method. Experimental results on different input duration using
the VoxCeleb1 test set demonstrate that the proposed technique improves speaker
verification performance by about 45.37% relatively compared to the baseline
system with 1-second test utterance condition.
