9 citations · 13 across the 12 of their papers we have counts for
4 papers · 1 filter
Multi-CMGAN+/+: Leveraging Multi-Objective Speech Quality Metric Prediction for Speech Enhancement
George Close, William Ravenscroft, Thomas Hain +1
Neural network based approaches to speech enhancement have shown to be particularly powerful, being able to leverage a data-driven approach to result in a significant performance g…
The Effect of Spoken Language on Speech Enhancement using Self-Supervised Speech Representation Loss Functions
George Close, Thomas Hain, Stefan Goetze
Recent work in the field of speech enhancement (SE) has involved the use of self-supervised speech representations (SSSRs) as feature transformations in loss functions. However, in…
Non Intrusive Intelligibility Predictor for Hearing Impaired Individuals using Self Supervised Speech Representations
George Close, Thomas Hain, Stefan Goetze
Self-supervised speech representations (SSSRs) have been successfully applied to a number of speech-processing tasks, e.g. as feature extractor for speech quality (SQ) prediction,…
Perceive and predict: self-supervised speech representation based loss functions for speech enhancement
George Close, William Ravenscroft, Thomas Hain +1
Recent work in the domain of speech enhancement has explored the use of self-supervised speech representations to aid in the training of neural speech enhancement models. However,…