  Creating universal speaker encoders which are robust for different acoustic
and speech duration conditions is a big challenge today. According to our
observations systems trained on short speech segments are optimal for short
phrase speaker verification and systems trained on long segments are superior
for long segments verification. A system trained simultaneously on pooled short
and long speech segments does not give optimal verification results and usually
degrades both for short and long segments. This paper addresses the problem of
creating universal speaker encoders for different speech segments duration. We
describe our simple recipe for training universal speaker encoder for any type
of selected neural network architecture. According to our evaluation results of
wav2vec-TDNN based systems obtained for NIST SRE and VoxCeleb1 benchmarks the
proposed universal encoder provides speaker verification improvements in case
of different enrollment and test speech segment duration. The key feature of
the proposed encoder is that it has the same inference time as the selected
neural network architecture.

Sound;Machine Learning;Audio and Speech Processing