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cs.SD2026
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…
cs.SD2025
The Eloquence team submission for task 1 of MLC-SLM challenge
Lorenzo Concina, Jordi Luque, Alessio Brutti +2
In this paper, we present our studies and experiments carried out for the task 1 of the Challenge and Workshop on Multilingual Conversational Speech Language Model (MLC-SLM), which…
cs.SD2025
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.…