GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
SPEECH PROCESSING AND RECOGNITION/BM-416
Course Title: SPEECH PROCESSING AND RECOGNITION
Credits 4 ECTS 6
Semester 8 Compulsory/Elective Elective
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Head of Computer Engineering Department
 -- WEB SITE(S) OF LECTURER(S)
  http://tf-bm.gazi.edu.tr/
 -- EMAIL(S) OF LECTURER(S)
  bm.tf@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
Ability to use theoretical and applied information in order to understand and implement algorithms of audio processing.
Ability to identify, formulate, and solve problems related with audio processing and speech recognition.
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 -- PREREQUISITES AND CO-REQUISITES
  There is no prerequisite or co-requisite for this course.
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  There is no recommended optional programme component for this course.
 --COURSE CONTENT
1. Week  Introduction/Overview of Audio Processing and Recognition
2. Week  Acoustics fundamentals: Sound, waves, waveguides, resonance, energy transfer.
3. Week  Basic Acoustic Features of Audio Signals (Volume, Zero Crossing Rate, Pitch, Timbre)
4. Week  Time-Frequency Representation of Audio Signals (Short-Term Discrete Fourier Transform)
5. Week  Voice activity detection - speech detection
6. Week  Pitch Tracking: Time domain (auto-correlation function, normalized squared difference function, average magnitude difference function)
7. Week  Pitch Tracking: Frequency-domain (harmonic product spectrum, Cepstrum method)
8. Week  Pitch Tracking: Postprocessing (Clipping & SIFT, Smoothing & Interpolation)
9. Week  Midterm Exam
10. Week  Introduction to Speech Recognition
11. Week  The speech recognition front end and pattern comparison techniques: Mel frequency cepstral co-efficients (MFCC).
12. Week  Statistical models for speech recognition: Hidden Markov Models and Viterbi Recognition
13. Week  Hidden Markov modeling for isolated word and continuous speech recognition
14. Week  Speech Recognition in practice: Using Hidden Markov ToolKit (HTK) for speech recognition
15. Week  Final Project Presentations
16. Week  Final Exam
 -- RECOMMENDED OR REQUIRED READING
  1) B. Gold and N. Morgan, Speech and Audio Signal Processing: Processing and Perception of Speech and Music, Wiley 2000. 2) L.R.Rabiner and R.W.Schafer, Digital Processing of Speech Signals, Prentice Hall 1978. 3) H. Perez-Meana, Advances in Audio and Speech Signal Processing : Technologies and Applications, IGI Publishing, 2006.
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
  Lecture, Question & Answer, Demonstration, Drill - Practise
 -- WORK PLACEMENT(S)
  No
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
1
20
 Assignment
1
10
 Exercises
0
0
 Projects
1
10
 Practice
0
0
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
40
 Contribution of Final Examination to Overall Grade  
60
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
14
3
42
 Practising Hours of Course Per Week
14
3
42
 Reading
0
 Searching in Internet and Library
14
3
42
 Designing and Applying Materials
0
 Preparing Reports
0
 Preparing Presentation
0
 Presentation
1
14
14
 Mid-Term and Studying for Mid-Term
1
6
6
 Final and Studying for Final
1
6
6
 Other
0
 TOTAL WORKLOAD: 
152
 TOTAL WORKLOAD / 25: 
6.08
 ECTS: 
6
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1Gaining the necessary theoretical and applied knowledge on engineering, mathematics, and science, skills for determining, defining and formulating computer engineering problems.X
2Gaining the ability to choose and apply appropriate analysis, modeling and design methods in computer engineering problems.X
3Gaining the ability to design a system, process or product related to computer engineering for a specific given purpose, gaining the ability to apply modern design tools.X
4Gaining the ability to evaluate the issues of security, robustness, adaptability, economy, ecological problems and sustainability in engineering solutions under realistic constraints and conditions.X
5Gaining the ability of simulation, experimenting, design, interpreting results for analysis and solution of computer engineering problems. Gaining the ability of analyzing of data for real problems which are need of industry.X
6Gaining the ability to use contemporary techniques and tools, information technologies for engineering applications.X
7Gaining the ability to work efficiently as individual or in a group in computer engineering discipline or in interdisciplinary studies. Gaining the ability to act independently, to use initiative when needed, and to be creative.X
8Gaining the ability to communicate efficiently by expressing his/her opinions in Turkish verbally or in written form in a concise manner. Gaining ability to efficiently use at least a foreign language in his/her proficiency.X
9Gaining the ability to grasp the significance of the concepts in areas such as business entrepreneurship, innovation and gaining ability for planning and management of a project.X
10Gaining the ability of awareness about self-renewal concept by comprehending the necessity of lifelong learning.X
11Gaining the ability to have professional and ethical responsibility.X
12The development of personality such as self-confidence, undaunting in the face of difficulties, consistency and patience.X
13Awareness about problems concerning with social, economic, environmental, etc. in our age and realization of the engineering profession by keeping mind in the responsibility which is related the awareness.X