I am a machine learning researcher with a focus on speech processing tasks.
I am currently working as a Speech Data Scientist at ConnexAI.
I hold a PhD in Computer Science from the University of Sheffield, and a BSc in Computer Science from Cardiff University.
My research interests include neural speech enhancement, automatic speech recognition, speech quality assessment and human perception of audio.
I an an expert in a number of deep learning frameworks including PyTorch and TensorFlow, and have extensive experience with GPU clusters and Linux systems.
Interests
Neural Speech Enhancement / Noise Reduction
Psychoacoustically motivated approaches
Automatic Speech Recognition (ASR)
Speech Quality / Intelligibility assessment metrics and prediction
Human perception of audio
Neural systems for hearing aids and other low power devices
PhD Computer Science + GTA Teaching Assisstant
Thesis Title: Perceptually Motivated Speech Enhancement
University of Sheffield, UK
October 2020 - January 2025
BSc Computer Science (First Class Honours)
Thesis Title: Majel - Voice control for Command Line Interfaces
Cardiff University, UK
August 2017- August 2020
Papers and Publications
I am an author on 12 papers, of which 9 I am first author.
Abstract: 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. In concert
with this is the topic of non-intrusive (i.e. without clean reference) speech quality prediction, for
which neural networks are trained to predict human-assigned quality labels directly from distorted
audio. When combined, these areas allow for the creation of powerful new speech enhancement systems
which can leverage large real-world datasets of distorted audio, by taking inference of a pre-trained
speech quality predictor as the sole loss function of the speech enhancement system. This paper aims to
identify a potential pitfall with this approach, namely hallucinations which are introduced by the
enhancement system `tricking' the speech quality predictor.
SSSR In Loss Functions For Hearing Aid Speech Enhancement EUSIPCO 2024 Robert Sutherland, George Close, Thomas Hain, Stefan Goetze and Jon Barker
Abstract: 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 signal.
Recent work has shown that feature encodings from self-supervised speech representation models can
effectively capture speech intelligibility. In this work, it is shown that the distance between
self-supervised speech representations of clean and noisy speech correlates more strongly with human
intelligibility ratings than other signal-based metrics. Experiments show that training a speech
enhancement model using this distance as part of a loss function improves the performance over using an
SNR-based loss function, demonstrated by an increase in HASPI, STOI, PESQ and SI-SNR scores. This method
takes inference of a high parameter count model only at training time, meaning the speech enhancement
model can remain smaller, as is required for hearing aids.
Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker
Automatic Speech Recognition arXiv | Interspeech 2024 William Ravenscroft, George Close, Stefan Goetze, Thomas Hain, Mohammad Soleymanpour, Anurag
Chowdhury and Mark C. Fuhs
Abstract: 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
artefacts which often degrade ASR performance. Addressing this issue typically requires reference
transcriptions to jointly train the separation and ASR networks. This is often not viable for training
on real-world in-domain audio where reference transcript information is not always available. This paper
proposes a transcription-free method for joint training using only audio signals. The proposed method
uses embedding differences of pre-trained ASR encoders as a loss with a proposed modification to
permutation invariant training (PIT) called guided PIT (GPIT). The method gives a 6.4% improvement in
word error rate (WER) measures over a signal-level loss and also shows enhancement improvements in
perceptual measures such as short-time objective intelligibility (STOI).
Non-Intrusive Speech Intelligibility Prediction for Hearing-Impaired Users using Intermediate ASR
Features and Human Memory Models
Techincal Report | arXiv | ICASSP 2024 🏆 2nd Place in Clarity Prediction Challenge
2 Rhiannon Mogridge, George Close, Robert Sutherland, Thomas Hain, Jon Barker, Stefan Goetze and
Anton Ragni
Abstact: Neural networks have been successfully used for non-intrusive speech intelligibility
prediction. Recently, the use of feature representations sourced from intermediate layers of pre-trained
self-supervised and weakly-supervised models has been found to be particularly useful for this task.
This work combines the use of Whisper ASR decoder layer representations as neural network input features
with an exemplar-based, psychologically motivated model of human memory to predict human intelligibility
ratings for hearing-aid users. Substantial performance improvement over an established intrusive HASPI
baseline system is found, including on enhancement systems and listeners unseen in the training data,
with a root mean squared error of 25.3 compared with the baseline of 28.7.
Multi-CMGAN+/+: Leveraging Multi-Objective Speech Quality Metric Prediction for Speech Enhancement
arXiv | ICASSP 2024 | GitHub George Close, Thomas Hain and Stefan Goetze
Abstract: 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 gain
versus other approaches. Such approaches are reliant on artificially created labelled training data such
that the neural model can be trained using intrusive loss functions which compare the output of the
model with clean reference speech. Performance of such systems when enhancing real-world audio often
suffers relative to their performance on simulated test data. In this work, a non-intrusive multi-metric
prediction approach is introduced, wherein a model trained on artificial labelled data using inference
of an adversarially trained metric prediction neural network. The proposed approach shows improved
performance versus state-of-the-art systems on the recent CHiME-7 challenge unsupervised domain
adaptation speech enhancement (UDASE) task evaluation sets.
Abstract: The CHiME-7 unsupervised domain adaptation speech enhancement (UDASE) challenge targets
in-domain adaptation to unlabelled speech data. This paper describes the University of Sheffield team’s
system submitted for the challenge. A generative adversarial network (GAN) structure is employed as op-
posed to the unsupervised RemixIT method proposed in the baseline system. The system uses a
conformer-based metric GAN (CMGAN) structure. The discriminator part of the GAN is trained to predict
the output of a DNSMOS model. Data aug- mentation strategies are employed which enable training on
historical training data as well as miscellaneous data from an additional generator. The proposed
approach, referred to as CM- GAN+/+, achieves significant improvement in DNSMOS evaluation metrics with
the best proposed system achieving 3.40 OVR-MOS, an 18% improvement over the baselines.
Non Intrusive Intelligibility Predictor for Hearing Impaired Individuals using Self Supervised
Speech Representations arXiv | SPARKS Workshop 2023 George Close, Thomas Hain and Stefan Goetze
Abstract: 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, which
is, in turn, relevant for assessment and training speech enhancement systems for users with normal or
impaired hearing. However, exact knowledge of why and how quality-related information is encoded well in
such representations remains poorly understood. In this work, techniques for non-intrusive prediction of
SQ ratings are extended to the prediction of intelligibility for hearing-impaired users. It is found
that self-supervised representations are useful as input features to non-intrusive prediction models,
achieving competitive performance to more complex systems. A detailed analysis of the performance
depending on Clarity Prediction Challenge 1 listeners and enhancement systems indicates that more data
might be needed to allow generalisation to unknown systems and (hearing-impaired) individuals.
The Effect of Spoken Language on Speech Enhancement using Self-Supervised Speech Representation Loss
Functions arXiv |
WASPAA 2023 |
GitHub George Close, Thomas Hain, Stefan Goetze
Abstact: 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
prior work, very little attention has been paid to the relationship between the language of the audio
used to train the self-supervised representation and that used to train the SE system. Enhancement
models trained using a loss function which incorporates a self-supervised representation that shares
exactly the language of the noisy data used to train the SE system show better performance than those
which do not match exactly. This may lead to enhancement systems which are language specific and as such
do not generalise well to unseen languages, unlike models trained using traditional spectrogram or time
domain loss functions. In this work, SE models are trained and tested on a number of different
languages, with self-supervised representations which themselves are trained using different language
combinations and with differing network structures as loss function representations. These models are
then tested across unseen languages and their performances are analysed. It is found that the training
language of the self-supervised representation appears to have a minor effect on enhancement
performance, the amount of training data of a particular language, however, greatly affects performance.
Perceive and predict: Self-Supervised Speech Representation Based Loss Functions for Speech
Enhancement arXiv |
ICASSP 2023 |
Audio Examples George Close, William Ravenscroft, Thomas Hain and Stefan Goetze
Abstract: 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, much of this
work focuses on using the deepest or final outputs of self supervised speech representation models,
rather than the earlier feature encodings. The use of self supervised representations in such a way is
often not fully motivated. In this work it is shown that the distance between the feature encodings of
clean and noisy speech correlate strongly with psychoacoustically motivated measures of speech quality
and intelligibility, as well as with human Mean Opinion Score (MOS) ratings. Experiments using this
distance as a loss function are performed and improved performance over the use of STFT spectrogram
distance based loss as well as other common loss functions from speech enhancement literature is
demonstrated using objective measures such as perceptual evaluation of speech quality (PESQ) and
short-time objective intelligibility (STOI).
PAMGAN+/-: Improving Phase Aware Speech Enhancement Performance via Expanded Discriminator
Training 154th AES Convention Europe
2023 🏆 WINNER: STUDENT TECHNICAL PAPER AWARD George Close, Thomas Hain and Stefan Goetze
Abstract: Recent speech enhancement work, which makes use of neural networks trained with a loss
derived in part using an adversarial metric prediction network, has shown to be very effective. However,
by limiting the data used to train this metric prediction network to only the clean reference and the
output of the speech enhancement network, only a limited range of the metric is learnt. Additionally,
such speech enhancement systems are limited because they typically operate solely over magnitude
spectrogram representations so they do not encode phase information. In this work, recent developments
for phase-aware speech enhancement in such an adversarial framework are expanded in two ways to enable
the metric prediction network to learn a full range of metric scores. Firstly, the metric predictor is
also exposed to unenhanced ’noisy’ data during training. Furthermore, an additional network is
introduced and trained alongside which attempts to produce outputs with a fixed ’lower’ target metric
score, and expose the metric predictor to these ’de-enhanced’ outputs. It is found that performance
increases versus a baseline system utilising a magnitude spectrogram speech enhancement network.
Non-intrusive Speech Intelligibility Metric Prediction for Hearing Impaired Individuals - Clarity
Prediction Challege 1
Interspeech 2022 George Close, Samuel Hollands, Thomas Hain and Stefan Goetze
Abstract: This paper proposes neural models to predict Speech Intelligibility (SI),both by
prediction of established SI metrics and of human speech recognition (HSR) on the 1st Clarity Prediction
Challenge. Both intrusive and non-intrusive predictors for intrusive SI metrics are trained, then fine
tuned on the HSR ground truth. Results are reported on a number of SI metrics, and the model choice for
the Clarity challenge submission is explained. Additionally, the relationship between the SI scores in
the data and commonly used signal processing metrics which approximate SI are analysed, and some issues
emerging from this relationship discussed. It is found that intrusive neural predictors of SI metrics
when finetuned on the true HSR scores outperform the non neural challenge baseline.
MetricGAN+/- Increasing Robustness of Noise Reduction on Unseen Data arXiv |
EUSIPCO 2022 |
Audio Examples George Close, Thomas Hain and Stefan Goetze
Abstract: Training of speech enhancement systems often does not incorporate knowledge of human
perception and thus can lead to unnatural sounding results. Incorporating psychoacoustically motivated
speech perception metrics as part of model training via a predictor network has recently gained
interest. However, the performance of such predictors is limited by the distribution of metric scores
that appear in the training data. In this work, we propose MetricGAN+/- (an extension of MetricGAN+, one
such metric-motivated system) which introduces an additional network - a "de-generator" which attempts
to improve the robustness of the prediction network (and by extension of the generator) by ensuring
observation of a wider range of metric scores in training. Experimental results on the VoiceBank-DEMAND
dataset show relative improvement in PESQ score of 3.8% (3.05 vs 3.22 PESQ score), as well as better
generalisation to unseen noise and speech.
