Information Package / Course Catalogue
EE 476 Introduction to Speech and Language Processing
MEF University
Degree Programs
Psychology
General Information For Students
Diploma SupplementErasmus Policy Statement
Psychology
Bachelor Length of the Programme: 4 Number of Credits: 240 TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF: Level 6

ECTS Course Information Package

School/Faculty/Institute Faculty of Engineering
Course Code EE 476
Course Title in English Introduction to Speech and Language Processing
Course Title in Turkish Konuşma ve Dil İşlemeye Giriş
Language of Instruction EN
Type of Course Select
Level of Course Select
Semester Spring
Contact Hours per Week
Lecture: 3 Recitation: Lab: Other:
Estimated Student Workload 149 hours per semester
Number of Credits 6 ECTS
Grading Mode Standard Letter Grade
Pre-requisites EE 204 - Signals and Systems
Co-requisites None
Expected Prior Knowledge Prior knowledge in discrete-time Fourier Transform, basics of probability and statistics is expected.
Registration Restrictions Only Undergraduate Students
Overall Educational Objective To learn the fundamentals of speech and language processing, and real-world speech and language processing applications.
Course Description This course provides an introduction to speech and language processing with a focus on real-world applications such as automatic speech recognition, spelling correction, information retrieval and text-to-speech. The following topics are covered: signal processing for speech, Hidden Markov Models, acoustic and language modeling, and basic building blocks of real world speech and language processing applications.

Course Learning Outcomes and Competences

Upon successful completion of the course, the learner is expected to be able to:
1) Understand the basic mechanisms of speech production and perception;
2) Analyze the speech signal using its time domain and frequency domain representations, and other representations in which it can be modeled;
3) Understand the fundamental methods and models used in language processing;
4) Use algorithms to built speech and language processing systems;
5) Describe some applications of statistical techniques used in (real-word) speech and language processing systems;
6) Prepare a well-organized written report and presentation for a speech and language processing application implementation;
7) Demonstrate team effort during the implementation of speech and language processing application.
Program Learning Outcomes/Course Learning Outcomes 1 2 3 4 5 6 7
1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology.
2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation.
3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes.
4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts.
5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline.
6) Internalization and dissemination of professional ethical standards.
7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences.
8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level).
9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity.
10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement.
11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses.
12) Ability to acquire knowledge independently, and to plan one’s own learning.
13) Demonstration of advanced competence in the clarity and composition of written work and presentations.

Relation to Program Outcomes and Competences

N None S Supportive H Highly Related
     
Program Outcomes and Competences Level Assessed by
1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. N
2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. N
3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. H Exam,HW,Participation
4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. N
5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. N
6) Internalization and dissemination of professional ethical standards. N
7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. N
8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). N
9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. S Participation
10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. S HW,Participation
11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. N
12) Ability to acquire knowledge independently, and to plan one’s own learning. S Exam,HW
13) Demonstration of advanced competence in the clarity and composition of written work and presentations. H Exam,HW
Prepared by and Date EBRU ARISOY SARAÇLAR , January 2020
Course Coordinator MEHMET FEVZİ ÜNAL
Semester Spring
Name of Instructor Asst. Prof. Dr. EBRU ARISOY SARAÇLAR

Course Contents

Week Subject
1) Introduction to Speech and Language Processing
2) Fundamentals: Sound and Human Speech Production -- Acoustical Model of Speech Production
3) Fundamentals: Hearing and Speech Perception
4) Speech Signal Representations
5) Speech Signal Representations
6) Speech Signal Representations
7) Algorithms for Estimating Speech Parameters
8) Regular Expressions, Text Normalization, Edit Distance
9) Language Modeling with N-grams
10) Vector Semantics
11) Hidden Markov Models
12) Hidden Markov Models
13) Speech and Language Processing Applications: Speech-to-Text (Speech Recognition)
14) Speech and Language Processing Applications: Spelling Correction & Noisy Channel
15) Final Exam/Project/Presentation Period
16) Final Exam/Project/Presentation Period
Required/Recommended ReadingsText books: 1) Rabiner and Schafer, Theory and Applications of Digital Speech Processing, Pearson Prentice Hall, 2011. 2) Jurafsky and Martin, Speech and Language Processing, 2nd edition. (See https://web.stanford.edu/~jurafsky/slp3/ for 3rd edition draft) Reference Text Books: Benesty, Sondhi, and Huang (Eds.), Springer Handbook of Speech Processing, Springer. Deller, Proakis, and Hansen, Discrete Time Processing of Speech Signals,IEEE Press. Huang, Acero and Hon, Spoken Language Processing, Prentice Hall. [HAH] Quatieri, Discrete-Time Speech Signal Processing, Prentice Hall. [Q] Rabiner and Juang, Fundamentals of Speech Recognition, Prentice Hall. [RJ] Rabiner and Schafer, Digital Processing of Speech Signals, Prentice Hall.
Teaching MethodsLectures/contact hours using “flipped classroom” as an active learning technique
Homework and ProjectsThere will be 4-5 homework assignments and a final project.
Laboratory WorkNone
Computer UseStudents will use software in some of their assignments and project.
Other ActivitiesNone
Assessment Methods
Assessment Tools Count Weight
Homework Assignments 5 % 25
Project 1 % 25
Midterm(s) 2 % 50
TOTAL % 100
Course Administration saraclare@mef.edu.tr
(0212) 3953677
Instructor’s office and phone number: 5th Floor, (0212) 3953677 office hours: TBA email address: saraclare@mef.edu.tr Rules for attendance: - Missing a midterm: Provided that proper documents of excuse are presented, a make-up exam will be given for each missed midterm. Missing a final: Faculty regulations. A reminder of proper classroom behavior, code of student conduct: YÖK Regulations Academic Dishonesty and Plagiarism: YÖK Regulations

ECTS Student Workload Estimation

Activity No/Weeks Hours Calculation
No/Weeks per Semester Preparing for the Activity Spent in the Activity Itself Completing the Activity Requirements
Course Hours 14 1 3 56
Project 1 29 2 31
Homework Assignments 5 4 4 40
Midterm(s) 2 10 1.5 23
Total Workload 150
Total Workload/25 6.0
ECTS 6