1 citations · 1 across the 10 of their papers we have counts for
10 papers
CHiME-9 ECHI: A Machine Learning Challenge for Enhancing Conversations to Address Hearing Impairment
Robert Sutherland, Thomas Kuebert, Marko Lugger +6
This work presents the task and results of the CHiME-9 challenge for Enhancing Conversations to address Hearing Impairment. The challenge considers the scenario of four-party conve…
Challenges of Multi-Speaker Extraction for Real Conversational Speech Enhancement
Robert Sutherland, Stefan Goetze, Jon Barker
Target-speaker and multi-speaker extraction are techniques for extracting speech from a desired speaker or desired speakers in the presence of other speakers and/or noise. Neural n…
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…
Speaker Embedding Informed Audiovisual Active Speaker Detection for Egocentric Recordings
Jason Clarke, Yoshihiko Gotoh, Stefan Goetze
Audiovisual active speaker detection (ASD) addresses the task of determining the speech activity of a candidate speaker given acoustic and visual data. Typically, systems model the…
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…
On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments
William Ravenscroft, Stefan Goetze, Thomas Hain
Speech separation remains an important topic for multi-speaker technology researchers. Convolution augmented transformers (conformers) have performed well for many speech processin…