Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
2EEE504DIGITAL SPEECH PROCESSING3+0+03619.06.2026

 
Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program ELECTRICAL AND ELECTRONICS ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course he aim of this course is to teach students the fundamental concepts and methods related to the production, digital representation, analysis, and processing of speech signals. Within the scope of the course, students are expected to gain knowledge of the speech production system, short-time analysis, spectral analysis, linear prediction, cepstral analysis, feature extraction, speech coding, speech recognition, speech synthesis, and speech enhancement. The course also aims to enable students to apply digital speech processing methods to engineering problems and interpret the obtained results.
Course Content Introduction to speech processing systems; human speech production mechanism; time-domain and frequency-domain characteristics of speech signals; sampling, quantization, and digital representation; short-time energy, zero-crossing rate, and autocorrelation analysis; short-time Fourier transform and spectrogram; linear prediction and LPC analysis; cepstral analysis and MFCC features; speech coding methods; speech enhancement and noise reduction; basic speech recognition methods; speech synthesis; speech processing applications and current approaches.
Course Methods and Techniques The course is conducted through lectures, problem solving, analysis of sample speech signals, computer-based applications, algorithm reviews, and homework/project assignments. MATLAB/Python-based applications, short-time analysis studies, feature extraction examples, and evaluations on real or sample speech datasets are used to help students understand speech processing methods.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Prof.Dr. Ergun Erçelebi ercelebi@gantep.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources O’Shaughnessy, D. (2000). Speech Communications: Human and Machine. IEEE Press.
Rabiner, L. R., & Schafer, R. W. (2011). Theory and Applications of Digital Speech Processing. Pearson.
Rabiner, L. R., & Schafer, R. W. (1978). Digital Processing of Speech Signals. Prentice Hall.
Course Notes Weekly lecture notes, study questions, application documents, and relevant chapters of the reference books.

Course Category
Mathematics and Basic Sciences %20
Engineering %50
Engineering Design %20
Field %10

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 30
Assignment 5 % 20
Project 1 % 20
Final examination 1 % 30
Total
8
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Weekly lecture hours 14 3 42
Reading Activities 14 2 28
Internet browsing, library work 5 3 15
Material design, application 7 4 28
Report preparation 1 12 12
Presentation preparation 1 6 6
Presentation 1 1 1
Midterm and midterm exam preparation 1 20 20
Final exam and preparation for the final exam 1 28 28
Total Work Load   Number of ECTS Credits 6 180

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain the speech production system, basic characteristics of speech signals, and their digital representation.
2 Analyze speech signals in the time and frequency domains.
3 Apply analysis methods such as short-time energy, zero-crossing rate, autocorrelation, and spectrogram.
4 Use basic feature extraction methods such as linear prediction, LPC, cepstral analysis, and MFCC.
5 Explain the basic principles of speech coding, speech enhancement, noise reduction, speech recognition, and speech synthesis methods.
6 Implement digital speech processing algorithms using tools such as MATLAB/Python.
7 Apply speech processing methods to engineering problems and evaluate the obtained results.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to speech processing and application areas Reviewing the course syllabus Lecutes notes, relevant textbook chapters
2 Human speech production system and speech perception Reviewing the speech production mechanism Lecutre notes and study questions
3 Digital representation of speech signals, sampling and quantization Reviewing the sampling concept Lecture notes and appiications documents
4 Time-domain analysis of speech signals Reviewing basic time-domain analysis Lecture notes and appiications documents
5 Short-time energy, zero-crossing rate and autocorrelation Reviewing short-time analysis concept Lecutre notes and study questions
6 Fourier analysis, STFT and spectrogram Reviewing Fourier transform Lecutes notes, relevant textbook chapters
7 Linear prediction and LPC analysis Reviewing linear prediction concepts Lecture notes and appiications documents
8 Midterm exam and general review of previous topics Preparation for the midterm exam Lecutre notes and study questions
9 Cepstral analysis and MFCC feature extraction Reviewing spectral features Lecture notes and appiications documents
10 Speech coding methods Reviewing coding methods Lecutes notes, relevant textbook chapters
11 Speech enhancement and noise reduction Reviewing noisy signals Lecture notes and appiications documents
12 Introduction to speech recognition systems Reviewing feature extraction and classification concepts Lecutre notes and study questions
13 Speech synthesis and current speech processing approaches Reviewing speech synthesis methods Lecutes notes, relevant textbook chapters
14 Project presentations, application evaluation and general review Preparing the project report and presentation Lecture notes and project documents

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4
All 4 4 3
C1 4 2 1
C2 4 4 2
C3 4 4 3
C4 5 4 3
C5 4 4 3
C6 4 4 4
C7 5 5 5

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