BÄ°L735 - SPEECH RECOGNITION

Course Name Code Semester Theory
(hours/week)		 Application
(hours/week)		 Credit ECTS
SPEECH RECOGNITION BÄ°L735 Any Semester/Year 3 0 3 8
PrequisitesNone.
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Preparing and/or Presenting Reports
Project Design/Management
 
Instructor (s)Department Responsible (bbm-bologna@cs.hacettepe.edu.tr) 
Course objectiveLearning of Speech Recognition basic concepts and developed of implementation areas. 
Learning outcomes
  1. Basic consepts of Speech recognition
  2. Speech recognition methods
  3. Application of speech recognition
Course ContentBasic consepts of Speech recognition. Speech Recognition algorithms. Language models, Application of speech recognition. 
ReferencesLawrence Rabiner, Fundamentals of Speech Recognition, Prentice Hall, 1993.
Frederick Jelinek, Statistical Methods for Speech Recognition (Language, Speech, and Communication), A Bradford Book, 1998 

Course outline weekly

WeeksTopics
Week 1Signal Processing and defining of speech data
Week 2Basic consepts of Speech Recognition
Week 3Speech Recognition techniques
Week 4Speech Recognition techniques (cont.)
Week 5Language definitions
Week 6Project I
Week 7Speech Recognition Implementations
Week 8Speech Recognition Implementations (cont.)
Week 9Case Study
Week 10Case Study (cont.)
Week 11Project II
Week 12Research presentations
Week 13Research presentations
Week 14Research presentations
Week 15Preparation to Final Exam
Week 16Final Exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments00
Presentation00
Project240
Seminar110
Midterms00
Final exam150
Total100
Percentage of semester activities contributing grade succes050
Percentage of final exam contributing grade succes050
Total100

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)9545
Presentation / Seminar Preparation13030
Project000
Homework assignment24080
Midterms (Study duration)000
Final Exam (Study duration) 11010
Total Workload2788207

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Graduates should have a mastery of computer science as described by the core of the Body of Knowledge.   X 
2. Graduates need understanding of a number of recurring themes, such as abstraction, complexity, and evolutionary change, and a set of general principles, such as sharing a common resource, security, and concurrency.   X  
3. Graduates of a computer science program need to understand how theory and practice influence each other.  X  
4. Graduates need to think at multiple levels of detail and abstraction.    X 
5. Students will be able to think critically, creatively and identify problems in their research.    X
6. Graduates should have been involved in at least one substantial project.     X
7. Graduates should realize that the computing field advances at a rapid pace.   X  
8. Graduates should conduct research in an ethical and responsible manner.  X   
9. Graduates should have good command of technical terms in both Turkish and English. X   
10. Graduates should understand the full range of opportunities available in computing.    X
11. Graduates should understand that computing interacts with many different domains.     X
12. Graduates should develop the knowledge acquired at master level and apply scientific methods in order to solve scientific problems.    X 

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest