TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree With Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	Associate Professor Mustafa Sert
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Know automata theory and general concepts. 2) Describe finite-state transducers. 3) Describes weighted automata algorithms. 4) Know speech recognition models. 5) Apply text-to-speech methods. 6) Apply the tecniques learned in an engineering problem.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	Introduction to speech recognition and introduction to the theory of automata. Automata-theoretic foundations.Introduction to the finite-state software tools. Rational relations. Transductions and weighted finite-state transducers.Weighted automata algorithms. Shortest-paths algorithms. Speech recognition by composition of weighted transducers.Models in speech recognition. Large vocabulary speech recognition. Text-to-speech
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction to speech recognition and introduction to the theory of automata 2nd Week Automata-theoretic foundations 3rd Week Introduction to the finite-state software tools 4th Week Rational relations 5th Week Rational relations 6th Week Transductions and weighted finite-state transducers 7th Week Weighted automata algorithms 8th Week Mid-term 9th Week Shortest-paths algorithms 10th Week Speech recognition by composition of weighted transducers 11th Week Speech recognition by composition of weighted transducers 12th Week Models in speech recognition 13th Week Large vocabulary speech recognition 14th Week Text-to-speech
RECOMENDED OR REQUIRED READING	1. Rabiner, L.R., Schafer, R.W., "Digital Processing of Speech Signals" Pearson Higher Education, (2011) 2. Rabiner, L.R., Juand, B.H., "Fundamentals of Speech Recognition", Prentice Hall, (1993) 3. Jelinek, F., "Statistical Methods For Speech Recognition", MIT Press, (1998)
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Questions/Answers,Presentation,Practice,Problem Solving,Project,Report Preparation
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Assignment 1 15 Project 1 15 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz Individual or group work 14 11 154 Preparation for Final exam 1 69 69 Course hours 14 3 42 Preparation for Midterm exam 1 44 44 Laboratory (including preparation) Final exam 1 2 2 Homework Total Workload 313 Total Workload / 30 10,43 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No