7 papers · 1 filter
WhiSQA: Non-Intrusive Speech Quality Prediction Using Whisper Encoder Features
George Close, Kris Hong, Thomas Hain +1
There has been significant research effort developing neural-network-based predictors of SQ in recent years. While a primary objective has been to develop non-intrusive, i.e.~refer…
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…
Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition
William Ravenscroft, George Close, Stefan Goetze +4
One solution to automatic speech recognition (ASR) of overlapping speakers is to separate speech and then perform ASR on the separated signals. Commonly, the separator produces art…
Hallucination in Perceptual Metric-Driven Speech Enhancement Networks
George Close, Thomas Hain, Stefan Goetze
Within the area of speech enhancement, there is an ongoing interest in the creation of neural systems which explicitly aim to improve the perceptual quality of the processed audio.…
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…
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…