2 citations · 6 across the 16 of their papers we have counts for
3 papers · 1 filter
Distillation-based Layer Dropping (DLD): Effective End-to-end Framework for Dynamic Speech Networks
Abdul Hannan, Daniele Falavigna, Shah Nawaz +3
Edge devices operate in constrained and varying resource settings, requiring dynamic architectures that can adapt to limitations of the available resources. To meet such demands, l…
Input Conditioned Layer Dropping in Speech Foundation Models
Abdul Hannan, Daniele Falavigna, Alessio Brutti
Curating foundation speech models for edge and IoT settings, where computational resources vary over time, requires dynamic architectures featuring adaptable reduction strategies.…
Scaling strategies for on-device low-complexity source separation with Conv-Tasnet
Mohamed Nabih Ali, Francesco Paissan, Daniele Falavigna +1
Recently, several very effective neural approaches for single-channel speech separation have been presented in the literature. However, due to the size and complexity of these mode…