Title: Differentiable Supervector Extraction for Encoding Speaker and Phrase Information in Text Dependent Speaker Verification,
Abstract: In this paper, we propose a new differentiable neural network alignment
mechanism for text-dependent speaker verification which uses alignment models
to produce a supervector representation of an utterance. Unlike previous works
with similar approaches, we do not extract the embedding of an utterance from
the mean reduction of the temporal dimension. Our system replaces the mean by a
phrase alignment model to keep the temporal structure of each phrase which is
relevant in this application since the phonetic information is part of the
identity in the verification task. Moreover, we can apply a convolutional
neural network as front-end, and thanks to the alignment process being
differentiable, we can train the whole network to produce a supervector for
each utterance which will be discriminative with respect to the speaker and the
phrase simultaneously. As we show, this choice has the advantage that the
supervector encodes the phrase and speaker information providing good
performance in text-dependent speaker verification tasks. In this work, the
process of verification is performed using a basic similarity metric, due to
simplicity, compared to other more elaborate models that are commonly used. The
new model using alignment to produce supervectors was tested on the
RSR2015-Part I database for text-dependent speaker verification, providing
competitive results compared to similar size networks using the mean to extract
embeddings.