This paper introduces a new method to extract speaker embeddings from a deep neural network (DNN) for text-independent speaker verification. Usually, speaker embeddings are extracted from a speaker-classification DNN that averages the hidden vectors over the frames of a speaker; the hidden vectors produced from all the frames are assumed to be equally important. We relax this assumption and compute the speaker embedding as a weighted average of a speaker's frame-level hidden vectors, and their weights are automatically determined by a self-attention mechanism. The effect of multiple attention heads are also investigated to capture different aspects of a speaker's input speech. Finally, a PLDA classifier is used to compare pairs of embeddings. The proposed self-attentive speaker embedding system is compared with a strong DNN embedding baseline on NIST SRE 2016. We find that the self-attentive embeddings achieve superior performance. Moreover, the improvement produced by the self-attentive speaker embeddings is consistent with both short and long testing utterances.

Mixup is a learning strategy that constructs additional virtual training samples from existing training samples by linearly interpolating random pairs of them. It has been shown that mixup can help avoid data memorization and thus improve model generalization. This paper investigates the mixup learning strategy in training speaker-discriminative deep neural network (DNN) for better text-independent speaker verification. In recent speaker verification systems, a DNN is usually trained to classify speakers in the training set. The DNN, at the same time, learns a low-dimensional embedding of speakers so that speaker embeddings can be generated for any speakers during evaluation. We adapted the mixup strategy to the speakerdiscriminative DNN training procedure, and studied different mixup schemes, such as performing mixup on MFCC features or raw audio samples. The mixup learning strategy was evaluated on NIST SRE 2010, 2016 and SITW evaluation sets. Experimental results show consistent performance improvements both in terms of EER and DCF of up to 13% relative. We further find that mixup training also improves the DNN's speaker classification accuracy consistently without requiring any additional data sources.

No abstract

No abstract