3 accepted papers
Knowledge distillation from Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) is a prominent training paradigm. However, its efficacy is fundamentally limited by a spectral mismatch: SNNs, with their intrinsic low-pass filtering characteristics, struggle to learn high-frequency det
Spiking Neural Networks (SNNs) promise significant energy efficiency by processing information via sparse, event-driven spikes. However, realizing this potential is hindered by the conventional use of a rigid, uniform timestep, T. This constraint imposes a challenging trade-off between accuracy and
Current speaker verification techniques heavily rely on the utilization of neural networks to extract accurate and discriminative speaker representations. In this paper, we present Branchformer based TDNN (B-TDNN), a novel architecture for extracting speaker embeddings by capturing both global and l…