Professor Thomas Hain
Head of the Speech and Hearing Research Group
Professor Hain is a world leader in speech recognition. He heads the Voicebase Centre for Speech and Language Technology and is a leader in the speech community.
"Talking and listening, understanding and expressive communication are skills that we all have. To this day we struggle to build machines that come close to human abilities.
"To explore and invent methods that allow us to recognise what is spoken, to understand, transform and interpret human communication has been the focus of my research. I am interested in machine learning methods that allow us to model communication and interaction, to be able to help people communicate, learn, and engage with new technology."
Examples of possible topics for supervision include:
Professor Rob Gaizauskas (currently on academic sabbatical)
Natural Language Processing Research Group
Professor Gaizauskas is internationally known for his research on information extraction and text mining, temporal information processing, question answering and summarisation.
"Can we build we build computer programs that understand human language? This question is of interest from both a cognitive science and linguistic perspective, and from an applied engineering perspective.
"What are the syntactic, semantic and pragmatic mechanisms available in human languages and how do intentional agents deploy them to communicate and accomplish goals in the world?
"How can we use our current, partial understanding of NLP to engineer applications that help people to gain better access to information in massive amounts of textual data and to dynamically interact with intelligent agents via NL dialogue?"
Example of possible topics for supervision include:
Independent Advisory Board
Research Supervisors and Associated Academics
Acting Co-Director, Supervisor, and Theme Lead for: Robust SLTs
Noise-robust speech recognition;
Hearing aid signal processing;
Perception of speech in noise;
Acoustic scene analysis.
Visiting Academic and external supervisor
Statistical and neural machine translation including linguistics aspects (factored neural machine translation)
Considering multiple modalities (multimodal neural machine translation).
Analysis of online misinformation and bots;
Hate speech and online abuse detection;
NLP methods for social media analysis, open source tools, information extraction, text analytics, social media summarisation, ethics and privacy in social media research.
Robustness to noise and reverberation;
Active hearing (eg, in robotic systems);
Clinical applications of speech technology.
Supervisor and Theme Lead for: Novel SLT applications
Speech recognition for atypical voices;
Audio and speech processing for assistive technology;
Pathological speech processing.
Developing effective retrieval technologies that support users as they seek to fulfil their information needs including multilingual search, retrieval of images, geo-spatial search, analysis of transaction logs, text re-use and plagiarism detection, and the evaluation of search systems.
Professor Hamish Cunningham
Language analysis infrastructure;
Text mining and textual big data processing;
Privacy-preserving social media;
Supervisor and Theme Lead for: Interconnecting SLT with the world
Digital signal processing;
Acoustic signal processing;
Speech and language processing;
Computational linguistics and natural language processing including
formal grammar and parsing,
clinical text mining,
temporal information processing,
robust dialogue processing,
efficient storage of large-scale linguistic data.
Supervisor and Theme Lead for: Scalable SLTs
Natural language processing and generation;
Social media analysis;
News and information bias;
Multidisciplinary work combining text analysis with behavioural and social information.
Spoken language processing;
Speech perception and production;
Clinical and creative applications of speech technology.
Online content verification (misinformation detection);
Quality estimation of machine translation;
Document-level evaluation of NLP tasks outputs;
Automatic construction of computational lexical resources.
Lexical semantics and analysis of word meanings (word sense disambiguation and lexical similarity);
Applications including medicine (text mining for systematic reviews, biomedical relation extraction, data mining and contradiction identification);
Document analysis (identification of text reuse, plagiarism and author identification);
Neural network word and phrase representation learning;
Idiomatic, figurative and metaphorical language;
Cognitive computational modelling;
Algorithms for language acquisition;
Processing and loss;
Language profiling in clinical conditions;
NLP for low-resourced languages;