  One of the most popular speaker embeddings is x-vectors, which are obtained
from an architecture that gradually builds a larger temporal context with
layers. In this paper, we propose to derive speaker embeddings from
Transformer's encoder trained for speaker classification. Self-attention, on
which Transformer's encoder is built, attends to all the features over the
entire utterance and might be more suitable in capturing the speaker
characteristics in an utterance. We refer to the speaker embeddings obtained
from the proposed speaker classification model as s-vectors to emphasize that
they are obtained from an architecture that heavily relies on self-attention.
Through experiments, we demonstrate that s-vectors perform better than
x-vectors. In addition to the s-vectors, we also propose a new architecture
based on Transformer's encoder for speaker verification as a replacement for
speaker verification based on conventional probabilistic linear discriminant
analysis (PLDA). This architecture is inspired by the next sentence prediction
task of bidirectional encoder representations from Transformers (BERT), and we
feed the s-vectors of two utterances to verify whether they belong to the same
speaker. We name this architecture the Transformer encoder speaker
authenticator (TESA). Our experiments show that the performance of s-vectors
with TESA is better than s-vectors with conventional PLDA-based speaker
verification.
