CourseSpecifications
Valid as from the academic year 2024-2025

Speech Processing (E010220)

Course size
Credits 4.0(nominal values; actual values may depend on programme)
Study time 120 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 2)
EnglishGent

Lecturers in academic year 2024-2025
Demuynck, Kris

lectureindependent work

TW06

lecturer-in-charge

Offered in the following programmes in 2024-2025
crdts
Master of Science in Electrical Engineering (main subject Communication and Information 4Technology )Master of Science in Computer Science4
Master of Science in Computer Science Engineering4Exchange Programme in Computer Science (master's level)
4offering
AAAA

Teaching languages
EnglishKeywords
Speech coding, speech recognition, speech synthesis
Position of the course
In this course, the basic principles underlying modern systems for speech recognition, speechcoding (e.g. GSM, MPEG), and speech synthesis are explained. Modern speech processingbuilds upon the principles of signal processing, machine learning (including deep learning), andartificial intelligence (search algorithms). In that aspect, this course also serves as an
introduction to (or recapitulation of) these techniques as well as an illustration of the applicationof these techniques in a specific domain.
Contents• Speech and hearing• Speech recognition• Digital speech reproduction• Speech synthesis
Initial competences
no foreknowledge required
Final competences
1 Get to know the properties of speech as an acoustic signal or as a carrier of linguistic
1 information.
2 Get to know the most important speech coding methods and understand the underlying
1 basic principles.
3 Get to know the major components of a speech recognition system and the way they these
1 components operate and interact with each other.
4 Get to know the major components of s speech synthesis system and the way these
1 components operate and interact
5 Be able to apply knowledge of speech production and perception to understand why certain
1 decisions were made during the design of speech processing systems.
6 Learn how to combine principles from signal processing, computational linguistics, machine
1 learning, and artificial intelligence in one system.
7 Learn when and how to speech processing technology can help to build new applications or(Approved)1

1 to improve the human-machine interaction in existing application.
8 Learn to understand the principles beyond the theory in such a way that you can also apply
1 them in other domains such as image processing or dynamic system modeling.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Lecture, Independent work
Study material
None
References• Spoken Language Processing; X. Huang, A. Acero, H. Hon; 2001; Prentice Hall (ISBN 0-13• 022616-5)
Course content-related study coaching
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Oral assessment open-book
Examination methods in case of periodic assessment during the second examination period
Oral assessment open-book
Examination methods in case of permanent assessment
Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible in modified form
Extra information on the examination methods
During examination period: oral open-book exam
During semester: graded project reports. Second chance: Possible in adapted form
Calculation of the examination markThe graded project report (during semester) counts for 1/4 of the points. The written exam
counts for the other 3/4 of the points.

(Approved)2