A Study of x-Vector Based Speaker Recognition on Short Utterances

Abstract

The aim of this work is to gain insights into how the deep neural network (DNN) models should be trained for short utterance evaluation conditions in an x-vector based speaker verification system. The study suggests that the speaker embedding can be extracted with reduced dimensions for short utterance evaluation conditions. When the speaker embedding is extracted from deeper layer which has lower dimension, the x-vector system achieves 14% relative improvement over baseline approach on EER on NIST2010 5sec-5sec truncated conditions. We surmise that since short utterances have less phonetic information speaker discriminative x-vectors can be extracted from a deeper layer of the DNN which captures less phonetic information. Another interesting finding is that the x-vector system achieves 5% relative improvement on NIST2010 5sec-5sec evaluation condition when the back-end PLDA is trained using short utterance development data. The results confirms the intuitive expectation that duration of development utterances and the duration of evaluation utterances should be matched. Finally, for the duration mismatch condition, we propose a variance normalization approach for PLDA training that provides a 4% relative improvement on EER over baseline approach.