Undergraduate Course: Speech Processing (Hons) (LASC10061)
Course Outline
| School | School of Philosophy, Psychology and Language Sciences |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | A 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 |
The course is delivered as a combination of lectures, flipped classrooms, an online forum, short videos, readings, and practical exercises in the lab. The first hour of each lecture is generally devoted to foundation material, making the course accessible to students from a wide variety of backgrounds, including Linguistics, Informatics, and Music Technology.
Students deciding whether to take this course should visit the lecturer's blog http://www.speech.zone where much of the course material can be found.
In the lab, students investigate speech signals, experiment with a text-to-speech system, and build their own simple automatic speech recognition system, using industry-standard tools.
Fundamentals of speech processing: familiarity with waveforms, spectra, spectrograms, resonance, formants, human speech production and perception., perceptually-motivated frequency scales, time vs. frequency representations; conversion between the two, the Fourier transform, source-filter model of speech, hands on experience.
Automatic Speech recognition: components of a typical recogniser, parameterisation of the speech signal, dynamic time warping, distance measures, the Hidden Markov Model, the generative model paradigm, simple probability theory, conditional and joint probabilities, Bayes theorem, Gaussian probability density function, continuous density HMMs, monophone models with Gaussian observation densities, Viterbi algorithm for recognition, training from fully labelled data, Viterbi training, bigram language models.
Text-to-speech synthesis: components of a typical text-to-speech synthesiser, text analysis, phonology, finite-state automata, POS tagging, lexicon, phrasing, accents, F0, learning from data, CART models, waveform generation, concatenative methods, TD-PSOLA and linear prediction, F0 and duration modification.
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Information for Visiting Students
| Pre-requisites | Visiting 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
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| Academic year 2021/22, Available to all students (SV1)
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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 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Assignment 1: 20%
Assignment 2: 30%
Assignment 3: 50%
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| Feedback |
Whole-class feedback on first coursework in form of written document and/or video |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- understand human speech production and perception, including the use of tools for visualising and manipulating speech
- give an overview of the components of automatic speech recognition and speech synthesis systems and describe a simple version of each component
- understand what the difficult problems are in automatic speech recognition and speech synthesis
- perform experiments with speech technology systems and relate theory to practice
- see how knowledge and skills from different areas come together in an interdisciplinary field
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Reading List
| http://resourcelists.ed.ac.uk/courses/lasc11065sv1sem1.html |
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 |
| Keywords | automatic speech recognition,text-to-speech synthesis,speech signal processing,phonetics |
Contacts
| Course organiser | Dr Catherine Lai
Tel: (0131 6)50 2698
Email: C.Lai@ed.ac.uk |
Course secretary | Mr Liam Hedley
Tel: (0131 6)50 9870
Email: liam.hedley@ed.ac.uk |
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