Psychology | |||||
Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
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 |
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Estimated Student Workload | 149 hours per semester | ||||||
Number of Credits | 6 ECTS | ||||||
Grading Mode | Standard Letter Grade | ||||||
Pre-requisites |
EE 204 - Signals and Systems |
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Expected Prior Knowledge | Prior knowledge in discrete-time Fourier Transform, basics of probability and statistics is expected. | ||||||
Co-requisites | None | ||||||
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 Description in Turkish | Bu ders konuşma ve dil işlemeye ve özellikle otomatik konuşma tanıma, yazılanı otomatik düzeltme, bilgi geri getirim ve konuşma sentezi gibi konuşma ve dil işlemenin gerçek hayatta kullanılan uygulamalarına kapsamlı bir giriş sağlamaktadır. Derste işlenen konular şu şekildedir: konuşma için sinyal işleme, Saklı Markov Modelleri, akustik ve dil modellemesi, gerçek hayatta kullanılan konuşma ve dil işleme uygulamalarını oluşturan temel kısımlar. |
Course Learning Outcomes and CompetencesUpon 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. |
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 |
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 Readings | Text 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 Methods | Lectures/contact hours using “flipped classroom” as an active learning technique | |||||||||||||||
Homework and Projects | There will be 4-5 homework assignments and a final project. | |||||||||||||||
Laboratory Work | None | |||||||||||||||
Computer Use | Students will use software in some of their assignments and project. | |||||||||||||||
Other Activities | None | |||||||||||||||
Assessment Methods |
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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 |
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 |