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

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

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

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.

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

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.

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, 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).

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.

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.

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