Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN441Fundamentals of Speech and Voice Recognition 3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Learning of Speech Recognition basic concepts and developed of implementation areas.
Course Content Basic consepts of Speech recognition. Speech Recognition algorithms. Language models, Application of speech recognition.
Course Methods and Techniques Lecture, Question and Answer, Demonstration, Drill and Practice.
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 )
Course Coordinator None
Name of Lecturers Associate Prof. Harun Artuner
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Lawrence Rabiner, Fundamentals of Speech Recognition, Prentice Hall, 1993. Frederick Jelinek, Statistical Methods for Speech Recognition (Language, Speech, and Communication), A Bradford Book, 1998
Course Notes Lawrence Rabiner, Fundamentals of Speech Recognition, Prentice Hall, 1993.

Frederick Jelinek, Statistical Methods for Speech Recognition (Language, Speech, and Communication), A Bradford Book, 1998


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
Midterm Exam 2 % 50
Final examination 1 % 50
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 6 84
Preparation for Midterm Exam 2 15 30
General Exam Preparation 1 20 20
Total Work Load   Number of ECTS Credits 5,86666666666667 176

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Basic concepts of Speech recognition
2 ·peech recognition methods
3 Application of speech recognition
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Course Overview and History, Articulatory Phonetics
2 Signal Processing and defining of speech data
3 Acoustic Phonetics
4 Speech and Sound Feature definations
5 Channel Model, HMMs, Forward, Viterbi, Word Error Rate
6 Recognition Models
7 Neural Network and Deep Learning models
8 1.st midterm
9 Introduction to Frame-Based Dialogue
10 Dialog Acts, Information State, and Markov Decision Processes
11 Conversational of Deep Learning Approaches
12 Social Meaning Extraction and Interpersonal Stance
13 2.nd midterm
14 Text to Speech (TTS): Overview, Text Normalization, Letter-to-Sound, Prosody
15 Far Field Speaker an Sound source Recognition
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 5 3 3 1 1 2 3 1 1 1
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://bilsis.hacettepe.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=2733671&lang=en