18 citations · 32 across the 9 of their papers we have counts for
9 papers
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,…
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…
MetricGAN+/-: Increasing Robustness of Noise Reduction on Unseen Data
George Close, Thomas Hain, Stefan Goetze
Training of speech enhancement systems often does not incorporate knowledge of human perception and thus can lead to unnatural sounding results. Incorporating psychoacoustically mo…
Joint Estimation of Reverberation Time and Direct-to-Reverberation Ratio from Speech using Auditory-Inspired Features
Feifei Xiong, Stefan Goetze, Bernd T. Meyer
Blind estimation of acoustic room parameters such as the reverberation time and the direct-to-reverberation ratio () is still a challenging task, espe…