Information Package / Course Catalogue
| Computer Engineering | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| 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) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | |||||||
| 2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | |||||||
| 3) An ability to communicate effectively with a range of audiences | |||||||
| 4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | |||||||
| 5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | |||||||
| 6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | |||||||
| 7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | H | Exam,HW,Project |
| 2) | An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | S | Exam,HW,Project |
| 3) | An ability to communicate effectively with a range of audiences | S | Project |
| 4) | An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | N | |
| 5) | An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | S | Project |
| 6) | An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | N | |
| 7) | An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. | S | HW,Project |
| 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 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 |
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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 | ||||||