Why study with us?

You will undertake high quality research that is relevant to the needs of industry alongside a comprehensive training programme addressing core and professional skills – gaining a Postgraduate Diploma (PGDip) in SLT Leadership integrated with your PhD.

The CDT offers a unique combination of industry-driven projects covering all areas of natural language processing (NLP) and speech processing research, including natural language analysis and generation, information retrieval, text mining (including sentiment analysis), question answering, machine translation, speech and speaker recognition, diarisation, machine hearing, novel methods of interaction and dialogue, and detection and analysis of paralinguistics.

You will receive supervision from a team of over 20 internationally leading SLT researchers, covering all core areas of modern SLT research, and a broader pool of over 50 academics in cognate disciplines with interests in SLTs and their application

As a student in the CDT, you will have a far superior level of support and training to that of a ‘conventional’ PhD studentship. You will receive 4 years of enhanced funding (including fees and a tax-free bursary of £17,000 per year), a generous personal research budget, specific training on core SLT expertise, industry-led teaching, CDT-wide cross-disciplinary mini-projects, and much more.

Find out more information by downloading our student brochure:

Our programme

Training is designed to lead you through a programme that will progressively expand your level of expertise, in research and professionally.

The bespoke cohort-based PGDip training programme running over the entire four years 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 first 6 months of the training programme will provide you with foundational SLT and general research training. During this time, you will also work with our industrial partners and academic supervisors to refine an industry-originated research project proposal. After this 6 month period, you will commence your PhD research.

You will complete a training needs analysis when you first arrive to help identify which programme of modules you should take. This will be revisited when you start your PhD research project.

An overview of the programme can be found below.

Four year training programme

The CDT training programme is organised into training years, where each year contains a range of specific activities.

Year 1
Skill Foundation

Year 1
Skill Foundation

SLT is exceptional in the range of disciplines 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 2
Scientific Foundation

Year 2
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 3
Research

Year 3
Research

This is expected to be the most productive research year. Activities will be similar to those conducted in Y2, however the students are expected to perform more leadership roles in cohort and team work, e.g., 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 Y2 or Y3.

Year 4
Consolidation, Presentation and Dissemination

Year 4
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 your 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. For details of the core and optional modules, please see below

Core Modules

You will complete the following modules:
Year 1

Introduction to Collaborative Research Practice for SLT

Introduction to Responsible SLT Leadership

Year 2

SLT Research and Leadership Practice 1: Scientific Foundation

Year 3

SLT Research and Leadership Practice 2: Core Research

Year 4

SLT Research and Leadership Practice 3: Dissemination and Impact

First Year Optional Modules

You will pick three from the following modules:
Scalable Machine Learning

This module will focus on technologies and algorithms that can be applied to data at a very large scale (e.g. population level). From a theoretical perspective it will focus on parallelization of algorithms and algorithmic approaches such as stochastic gradient descent. There will also be a significant practical element to the module that will focus on approaches to deploying scalable ML in practice such as SPARK, FLINK, programming languages such as Scala and deployment on elastic computing structures, cloud computing and/or GPUs.

Text Processing

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.

Speech Processing

This module aims to demonstrate why computer speech processing is an important and difficult problem, 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, psychoacoustics, etc.). It introduces sufficient digital signal processing (linear systems theory, Fourier transforms) to motivate speech parameter extraction techniques (e.g. pitch and formant tracking).

Machine Learning and Adaptive Intelligence

The 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. The module will give students a grounding in modern state of the art algorithms that allow modern computer systems to learn from data.

Speech Technology

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. The module will include project work involving the implementation and assessment of a speech technology device.

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’s 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

Sponsored by