[PDF][PDF] ECAPA++: Fine-grained deep embedding learning for TDNN based speaker verification
In this paper, we aim to bridge the performance gap between TDNN and 2D CNN based
speaker verification systems. Specifically, three types of architectural enhancements to …
speaker verification systems. Specifically, three types of architectural enhancements to …
Towards Lightweight Speaker Verification via Adaptive Neural Network Quantization
Modern speaker verification (SV) systems typically demand expensive storage and
computing resources, thereby hindering their deployment on mobile devices. In this paper …
computing resources, thereby hindering their deployment on mobile devices. In this paper …
Memory-Efficient Training for Deep Speaker Embedding Learning in Speaker Verification
Recent speaker verification (SV) systems have shown a trend toward adopting deeper
speaker embedding extractors. Although deeper and larger neural networks can …
speaker embedding extractors. Although deeper and larger neural networks can …
Lowbit neural network quantization for speaker verification
With the continuous development of deep neural networks (DNN) in recent years, the
performance of speaker verification systems has been significantly improved with the …
performance of speaker verification systems has been significantly improved with the …