3 papers
eess.AS2026
SlimDiffuSE: Towards Efficient Diffusion-Based Speech Enhancement using Slimmable Networks
Nagashree K. S. Rao, Shrishti Saha Shetu, Mohamed Elminshawi +2
Diffusion-based models are emerging in the speech enhancement domain and are achieving state-of-the-art performance across various benchmark datasets. A major downside of diffusion…
eess.AS2025
Training Strategies for Modality Dropout Resilient Multi-Modal Target Speaker Extraction
Srikanth Korse, Mohamed Elminshawi, Emanuel A. P. Habets +1
The primary goal of multi-modal TSE (MTSE) is to extract a target speaker from a speech mixture using complementary information from different modalities, such as audio enrolment a…
eess.AS2025
Dynamic Slimmable Networks for Efficient Speech Separation
Mohamed Elminshawi, Srikanth Raj Chetupalli, Emanuël A. P. Habets
Recent progress in speech separation has been largely driven by advances in deep neural networks, yet their high computational and memory requirements hinder deployment on resource…