In addition to your research project, you will undertake a comprehensive training programme addressing core technical, research, and professional skills – gaining a Postgraduate Diploma (PGDip) in SLT Leadership integrated with your PhD.
This bespoke programme runs over the entire four years and provides you with the necessary skills for academic and industrial leadership in the field, based on elements covering core SLT skills, research software engineering (RSE), ethics, innovation, entrepreneurship, management, and societal responsibility.
The training is front-loaded to allow you to gain more technical and research skills before you start your research project in the second half of the first year.
Find out more information by viewing our student brochure.
Let’s take a look at each year
Year one - skill foundation
SLT is exceptional in the range of disciplines with which it draws upon, from linguistics and phonetics through mathematics and computer science to signal processing and electrical engineering.
The first year is therefore designed to ensure that the group of students enrolling from diverse academic backgrounds can develop into a well-integrated, self-supporting cohort. In particular, the programme launches with a three day intensive workshop that will provide students with a shared understanding of the ethos of the CDT and an appreciation of the broader SLT research landscape.
Students receive unconscious bias and ED&I training to perpetuate an environment of fairness, equality, diversity and respect.
Training in the first semester will then be devoted to bringing students up to a similar skill level across a range of foundational topics. After this induction phase, student PhD projects will be defined in discussion with the students, and supervisors assigned.
Students start work on their research topic while still receiving foundational training.
Year two - scientific foundation
The second year is devoted to developing advanced SLT research skills in practice, to perform the first foundational experiments and to formulate the plan for the PhD.
Students are expected to submit a PhD transfer report to a confirmation panel which will assess the quality of the research and the suitability and viability of the research plan for successful PhD study.
The student will engage in further cohort and external activities as well as receive further training in all training domains and modes.
Year three - research
This is expected to be the most productive research year.
Activities will be similar to those conducted in year two, however the students are expected to perform more leadership roles in cohort and team work. This is achieved by supervising mini-projects, by stepping into planning roles in the SLT hub or SLT Challenge activities, or by mentoring of peers.
Internships are likely to happen in year two or year three.
Year four - consolidation, presentation and dissemination
In the final year, the emphasis will be on thesis completion and on ensuring impact through presentation or realisation in practical settings. Examples are writing and presentation, responsibility assessment and ethical re-evaluation, proposal writing or entrepreneurial activities.
The year will end first with the completion of the required credits to receive the PGDip, followed by submission and assessment of the PhD thesis.
PGDip taught modules
The programme requires the completion of 120 credits of modules over the four year course.
In your first year, you will study 75 credits – two 15 credit core modules, plus three 15 credit optional modules. In your second, third and fourth years, you will study one 15 credit core module per year.
Core CDT modules
You will complete the following modules:
Introduction to Collaborative Research Practice for SLT
Introduction to Responsible SLT Leadership
SLT Research and Leadership Practice 1: Scientific Foundation
SLT Research and Leadership Practice 2: Core Research
SLT Research and Leadership Practice 3: Dissemination and Impact
First year optional modules
You will pick three from the following modules; alternatives are available if you've already covered this material in your previous degree(s):
Scalable machine learning
This module will focus on technologies and algorithms that can be applied to data at a very large scale, such as population level. From a theoretical perspective it will focus on parallelisation of algorithms and algorithmic approaches such as stochastic gradient descent.
This module introduces fundamental concepts and ideas in natural language text processing, covers techniques for handling text corpora, and examines representative systems that require the automated processing of large volumes of text. The course focuses on modern quantitative techniques for text analysis and explores important models for representing and acquiring information from texts.
This module aims to demonstrate why computer speech processing is an important and difficult problem. It aims to investigate the representation of speech in the articulatory, acoustic and auditory domains, and to illustrate computational approaches to speech parameter extraction.
It examines both the production and perception of speech, taking a multi-disciplinary approach (drawing on linguistics, phonetics and psychoacoustics). It introduces sufficient digital signal processing (linear systems theory, Fourier transforms) to motivate speech parameter extraction techniques such as pitch and formant tracking.
Machine learning and adaptive intelligence
This module is about core technologies underpinning modern artificial intelligence. The module will introduce statistical machine learning and probabilistic modelling and their application to describing real world phenomena.
This module introduces the principles of the emergent field of speech technology, studies typical applications of these principles and assesses the state of the art in this area. Students will learn the prevailing techniques of automatic speech recognition (based on statistical modelling); will see how speech synthesis and text-to-speech methods are deployed in spoken language systems; and will discuss the current limitations of such devices.
Natural language processing
This module provides an introduction to the field of computer processing of written natural language, known as Natural Language Processing (NLP). We will cover standard theories, models and algorithms, discussing competing solutions to problems, describing example systems and applications, and highlighting areas of open research.
The content of our courses is reviewed annually to make sure it is up to date and relevant. Individual modules are occasionally updated or withdrawn.
This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers.
In the event of any change we’ll consult and inform students in good time and take reasonable steps to minimise disruption.