Title: Unified Hypersphere Embedding for Speaker Recognition,
Abstract: Incremental improvements in accuracy of Convolutional Neural Networks are
usually achieved through use of deeper and more complex models trained on
larger datasets. However, enlarging dataset and models increases the
computation and storage costs and cannot be done indefinitely. In this work, we
seek to improve the identification and verification accuracy of a
text-independent speaker recognition system without use of extra data or deeper
and more complex models by augmenting the training and testing data, finding
the optimal dimensionality of embedding space and use of more discriminative
loss functions. Results of experiments on VoxCeleb dataset suggest that: (i)
Simple repetition and random time-reversion of utterances can reduce prediction
errors by up to 18%. (ii) Lower dimensional embeddings are more suitable for
verification. (iii) Use of proposed logistic margin loss function leads to
unified embeddings with state-of-the-art identification and competitive
verification accuracies.