THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2026/2027

Timetable information in the Course Catalogue may be subject to change.

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Language Sciences

Undergraduate Course: Speech Synthesis (LASC10062)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course covers the current state-of-the-art in speech synthesis. The course starts with the historical context, so that students understand how we arrived at the state-of-the art, and concludes with the most recent neutral-network systems. The course also provides some foundation material on machine learning and neutral networks., and coverage of both classical and machine-learning-based speech signal processing, as used for speech recording and synthesis. The course may also touch on issues surrounding speech synthesis, such as its use in creating deepfakes.
Course description This course is delivered using a variety of teaching and learning methods. The early part of the course uses a flipped classroom in which students watch videos and do readings, which then form the basis of the interactive weekly class. The later part of the course involves reading recent research papers, which are then elaborated and discussed in class. A single practical assignment, performed in weekly supervised computer lab sessions, forms and integral part of the course. In the assignment, students record themselves in a professional recording studio, then use their recordings to build a neutral speech synthesiser, which they subsequently evaluate using a listening test. The coursework is written up in the style of a journal paper.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Speech Processing (Hons) (LASC10061)
Co-requisites
Prohibited Combinations Students MUST NOT also be taking Speech Synthesis (LASC11062)
Other requirements Students MUST NOT have previously taken or be currently taking LASC11062 Speech Synthesis.
Additional Costs None
Information for Visiting Students
Pre-requisitesVisiting students should have completed at least 3 Linguistics/Language Sciences courses at grade B or above . We will only consider University/College level courses.
High Demand Course? Yes
Course Delivery Information
Academic year 2026/27, Available to all students (SV1) Quota:  0
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 22, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 154 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) Lab report worth 50% - Written report based on practical work in the computing lab (4000 word)s.
Centrally-arranged exam worth 50% (2 hours)

Feedback Class-wide formative feedback will be provided based on work previously submitted for Speech Processing (from multiple students, anonymised), since the style and format are similar. This feedback takes the form of videos, slides, and blog posts, with follow-up questions answered via the course forum, in class, and in lab sessions.

Comments will be provided on submitted coursework. A structured marking scheme will be used. All students will have the opportunity for an individual 15-minute summative feedback session with the course organiser, after it is returned.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand speech synthesis methods currently in use, and the historical developments that underpin them.
  2. Be familiar with speech signal processing and coding techniques that are used for speech synthesis.
  3. Have the practical experience of building a synthetic voice.
  4. Be able to discuss current issues in speech synthesis and be well-placed to understand future developments.
  5. Have improved their scientific written communication skills.
Reading List
Indicative reading list:

Paul Taylor Text-to-speech synthesis, 2009, Cambridge University Press, Cambridge

Chengyi Wang et al. Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers DOI: 10.48550/arXiv.2301.02111

Latest information is on the course webpage https://speech.zone/courses/speech-synthesis
Additional Information
Graduate Attributes and Skills The course materials require students to read and evaluate research papers with a critical eye. Classes are interactive and involve a little in-class group work such as discussion papers, or elements of the practical assignment. In that practical assignment, students record speech in a professional recording studio and use it to build a synthetic voice. Good time and workload management are required to balance the various competing elements of the assignment. The instructions are deliberately somewhat under-specified, to encourage students to develop their own independent preparation, planning, organisation, and execution skills. The assignment is written up in the style of a journal paper.

Core skills gained or developed on this course: Critical thinking, analysis, and evaluation; Data collection and analysis; Enhanced programming / coding skills; Independence; Preparation, planning and organisation; Problem solving; Academic reading skills; Report writing; Research skills; Resilience; Time management; Workload management; Written communication; Writing clearly and concisely; Critical reading of recent research publications; Scientific writing, following a journal style guide; Designing and implementing an original algorithm; Using a professional recording studio; Experimental design, including the use of human subjects for perceptual testing.

Keywords: speech synthesis, machine learning, neutral networks, signal processing.
Additional Class Delivery Information 10 x 2 hour lectures and 11 x 2 hour practical sessions.
Keywordsspeech synthesis,machine learning,neural networks,signal processing
Contacts
Course organiserProf Simon King
Tel: (0131 6)51 1725
Email: Simon.King@ed.ac.uk
Course secretaryMiss Kayla Johnson-McCraw
Tel: (0131 6)50 3440
Email: Kayla.Johnson@ed.ac.uk
Navigation
Help & Information
Home
Introduction
Glossary
Search DPTs and Courses
Regulations
Regulations
Degree Programmes
Introduction
Browse DPTs
Courses
Introduction
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Prospectuses
Important Information