9 citations · 12 across the 9 of their papers we have counts for
9 papers
Whilter: A Whisper-based Data Filter for "In-the-Wild" Speech Corpora Using Utterance-level Multi-Task Classification
William Ravenscroft, George Close, Kit Bower-Morris +3
Large-scale in-the-wild speech datasets have become more prevalent in recent years due to increased interest in models that can learn useful features from unlabelled data for tasks…
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…
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…
On Data Sampling Strategies for Training Neural Network Speech Separation Models
William Ravenscroft, Stefan Goetze, Thomas Hain
Speech separation remains an important area of multi-speaker signal processing. Deep neural network (DNN) models have attained the best performance on many speech separation benchm…
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,…
Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation
William Ravenscroft, Stefan Goetze, Thomas Hain
Speech separation models are used for isolating individual speakers in many speech processing applications. Deep learning models have been shown to lead to state-of-the-art (SOTA)…
Utterance Weighted Multi-Dilation Temporal Convolutional Networks for Monaural Speech Dereverberation
William Ravenscroft, Stefan Goetze, Thomas Hain
Speech dereverberation is an important stage in many speech technology applications. Recent work in this area has been dominated by deep neural network models. Temporal convolution…
Receptive Field Analysis of Temporal Convolutional Networks for Monaural Speech Dereverberation
William Ravenscroft, Stefan Goetze, Thomas Hain
Speech dereverberation is often an important requirement in robust speech processing tasks. Supervised deep learning (DL) models give state-of-the-art performance for single-channe…