Deep neural network based speaker embeddings for end to end speaker verification
In this study, we investigate an end to end text independent speaker verification system. The architecture consists of a deep neural network that takes a variable length speech segment and maps it to a speaker embedding. The objective function separates same speaker and different speaker pairs, and is reused during verification. Similar systems have recently shown promise for text dependent verification, but we believe that this is unexplored for the text independent task. We show that given a large number of training speakers, the proposed system outperforms an i vector baseline in equal error rate (EER) and at low miss rates. Relative to the baseline, the end to end system reduces EER by 13% average and 29% pooled across test conditions. The fused system achieves a reduction of 32% average and 38% pooled.
2016 IEEE Spoken Language Technology Workshop (SLT)