Title: On deep speaker embeddings for text-independent speaker recognition,
Abstract: We investigate deep neural network performance in the textindependent speaker
recognition task. We demonstrate that using angular softmax activation at the
last classification layer of a classification neural network instead of a
simple softmax activation allows to train a more generalized discriminative
speaker embedding extractor. Cosine similarity is an effective metric for
speaker verification in this embedding space. We also address the problem of
choosing an architecture for the extractor. We found that deep networks with
residual frame level connections outperform wide but relatively shallow
architectures. This paper also proposes several improvements for previous
DNN-based extractor systems to increase the speaker recognition accuracy. We
show that the discriminatively trained similarity metric learning approach
outperforms the standard LDA-PLDA method as an embedding backend. The results
obtained on Speakers in the Wild and NIST SRE 2016 evaluation sets demonstrate
robustness of the proposed systems when dealing with close to real-life
conditions.