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Found 5 papers, 1 papers with code
Restorative Speech Enhancement: A Progressive Approach Using SE and Codec Modules
In challenging environments with significant noise and reverberation, traditional speech enhancement (SE) methods often lead to over-suppressed speech, creating artifacts during listening and harming downstream tasks performance.
Multi-objective Non-intrusive Hearing-aid Speech Assessment Model
Furthermore, we demonstrated that incorporating SSL models resulted in greater transferability to OOD dataset.
Study on the Correlation between Objective Evaluations and Subjective Speech Quality and Intelligibility
Moreover, new objective measures are proposed that combine current objective measures using deep learning techniques to predict subjective quality and intelligibility.
Boosting Self-Supervised Embeddings for Speech Enhancement
We further study the relationship between the noise robustness of SSL representation via clean-noisy distance (CN distance) and the layer importance for SE.
Ranked #14 on
Speech Enhancement
on VoiceBank + DEMAND
HASA-net: A non-intrusive hearing-aid speech assessment network
Without the need of a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations.
