3 papers
cs.SD2024
Using Speech Foundational Models in Loss Functions for Hearing Aid Speech Enhancement
Robert Sutherland, George Close, Thomas Hain +2
Machine learning techniques are an active area of research for speech enhancement for hearing aids, with one particular focus on improving the intelligibility of a noisy speech sig…
cs.SD2024
Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge
Simon Leglaive, Matthieu Fraticelli, Hend ElGhazaly +7
Supervised models for speech enhancement are trained using artificially generated mixtures of clean speech and noise signals. However, the synthetic training conditions may not acc…
cs.SD2024
Non-Intrusive Speech Intelligibility Prediction for Hearing-Impaired Users using Intermediate ASR Features and Human Memory Models
Rhiannon Mogridge, George Close, Robert Sutherland +4
Neural networks have been successfully used for non-intrusive speech intelligibility prediction. Recently, the use of feature representations sourced from intermediate layers of pr…