3 accepted papers
In this paper, a multi-scale feature fusion paradigm is proposed to fully exploit the power of the pre-trained models for text-independent speaker verification. It contains a front-end feature extractor and an enhanced ECAPA-TDNN backend in a cascade manner. The feature extractor incorporates local…
In this paper, we propose a noise-invariant speaker representation learning (SRL) approach by aligning noisy-clean speech pairs at both the feature and embedding levels for model training. Specifically, we first construct noisy-clean pairs using data augmentation during training. The noisy features…
It was recently revealed that using features extracted from pre-trained models can achieve much better performance than using conventional hand-crafted acoustic features for spoofing speech detection. In this paper, we therefore enhance the features from pre-trained model based on recursive learning…