2026/27 Undergraduate Module Catalogue

LING3390 Linguistic Technologies

20 Credits Class Size: 20

Module manager: Dr Elliot Holmes
Email: e.j.holmes@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2026/27

Pre-requisite qualifications

MODL1060 Language: Structure and Sound or equivalent Students who have not completed MODL1060 Language: Structure and Sound should be prepared to do some additional reading to familiarise themselves with linguistic concepts built on in this module. Chapters 1 to 6 of Genetti’s How languages work: An introduction to language and linguistics (Cambridge University Press, 2014) are a good starting point.

Module replaces

LING3380 Linguistic Innovations in Technology

This module is not approved as a discovery module

Module summary

How do computers hear, understand, and speak human language? Where do linguists fit into that process? Linguistic Technologies helps you discover how your Linguistics degree can open doors in rapidly developing fields such as forensic linguistics, speech and language therapy, accessibility, and artificial intelligence. Language technologies are becoming central to our daily lives, from our voice assistants and translation apps to our surveillance, healthcare diagnostics, and legal evidence. As a result, the need for linguists who can analyse, critique, and improve these systems has never been greater. Please note this is an optional module and runs subject to enrolments. If a low number of students choose this module, then the module may not run and you may be asked to choose another module.

Objectives

Across the module, students will uncover the linguistic principles that underpin the technologies that we use every day. The module covers speech and speaker recognition, machine translation, live subtitling, and real-world forensic, military, and clinical linguistic technologies. The module is designed to be fully accessible to all Linguistic students. Each topic begins from linguistic theory that students already know, then expands on it through tech demonstrations in the lectures and hands-on experiments in the practicals. Students don’t need to know how to code; students here to learn how these systems work, what assumptions they make about language, and how they as linguists could improve them and make them fairer, clearer, and more human. By the end, students will understand how language technologies shape modern communication and how linguists can shape them in return. Students will leave with analytical, ethical, and communication skills valued across forensic analysis, healthcare communication, defence, accessibility design, and the language-focused areas of AI research and development.

Learning outcomes

On successful completion of the module students will be able to: 1. Analyse how linguistic technologies process and generate language, interpreting and critiquing their outputs using linguistic concepts. 2. Evaluate how linguistic knowledge can inform technology‑related issues, drawing on interdisciplinary perspectives and research–industry contexts. 3. Conduct research involving the quantitative analysis of real-world data using appropriate digital tools. 4. Communicate analytical findings effectively and critically.

Teaching Methods

Delivery type Number Length hours Student hours
Lecture 10 1 10
Practical 10 1 10
Private study hours 180
Total Contact hours 20
Total hours (100hr per 10 credits) 200

Private study

180

Opportunities for Formative Feedback

Each practical will involve the use of linguistic technologies such as speaker recognition systems and generative A.I. systems. Students will receive formative feedback in class as to the effectiveness of their approaches. Preparation for each practical will involve guided instructions on the practical use of these technologies, with the assistance of the module leader. Students will also, in one practical session, give a formative presentation on how they have employed language-related technologies. These practical sessions relate directly to the assessment.

Methods of Assessment

Coursework
Assessment type Notes % of formal assessment
Coursework Critique 40
Coursework Investigative Project 60
Total percentage (Assessment Coursework) 100

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading List

Check the module area in Minerva for your reading list

Last updated: 30/04/2026

Errors, omissions, failed links etc should be notified to the Catalogue Team