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DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Language Sciences

Undergraduate Course: Speech Processing (Hons) (LASC10061)

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
SummaryA foundation course in speech processing for students of linguistics, informatics, and related subjects.

Enrolments for students outwith Philosophy, Psychology and Language Sciences must be approved by the Course Organiser.
Course description In this course, students investigate the fundamentals of speech signal processing, text-to-speech systems, and automatic speech recognition.

This is a fast-paced course which draws on topics from linguistics, informatics, and engineering, including the following:

Foundations in phonetics and signal processing: sound and waveforms, the connection between speech acoustics and speech articulation, time versus frequency representations of speech, the Discrete Fourier Transform and frequency spectrums, the source-filter model of speech.
Text-to-speech synthesis: components of a concatenative text-to-speech synthesiser, introduction to automatic text analysis, waveform generation and speech modification methods.

Automatic Speech recognition: components of a typical speech recognition system, speech feature representations, measuring similarity in speech using probabilistic methods, Hidden Markov Models (HMMs) for ASR, training and testing probabilistic models with speech data.

The course is delivered as a combination of lectures, an online forum, videos, readings, and practical exercises in the lab. The course work will be based on lab exercises where students investigate an existing text-to-speech system, and build a simple automatic speech recognition system. Students deciding whether to take this course should visit where much of the course material can be found.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: ( Informatics 2 - Foundations of Data Science (INFR08030) OR LEL2B: Phonetic Analysis and Empirical Methods (LASC08018)) AND
Prohibited Combinations Other requirements The course content involves some mathematical concepts and the course work will involve some coding (e.g. Python and Unix shell scripting). Though we will primarily take a conceptual approach, some background in basic linear algebra and probability will be beneficial, as will some experience in using the Unix command line.

Enrolments for students outwith Philosophy, Psychology and Language Sciences must be approved by the Course Organiser.
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 2023/24, Available to all students (SV1) Quota:  0
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Supervised Practical/Workshop/Studio Hours 20, Online Activities 60, Feedback/Feedforward Hours 2, Summative Assessment Hours 4, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 110 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 30% online tests
30% assignment (lab report 1 1500 max words)
40% assignment (lab report 2 3000 max words)
Feedback Multiple choice question quizzes will be automatically graded and solutions will be discussed in class and/or on the online forum. Comments will be provided for submitted coursework.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. understand human speech production and perception, including the use of tools for visualising and manipulating speech
  2. give an overview of the components of automatic speech recognition and speech synthesis systems and describe a simple version of each component
  3. understand what the difficult problems are in automatic speech recognition and speech synthesis
  4. perform experiments with speech technology systems and relate theory to practice
  5. see how knowledge and skills from different areas come together in an interdisciplinary field
Reading List
Additional Information
Graduate Attributes and Skills Ability to use standard speech processing packages including Wavesurfer, Praat, Festival and HTK
Basic shell scripting
Scientific writing
Experimental design
Keywordsautomatic speech recognition,text-to-speech synthesis,speech signal processing,phonetics
Course organiserDr Catherine Lai
Tel: (0131 6)50 2698
Course secretaryMs Susan Hermiston
Tel: (0131 6)50 3440
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