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| Course Description |
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| Course Name |
: |
Speech Processing II |
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| Course Code |
: |
CENG-560 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Spring (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
: |
Assoc.Prof.Dr. ZEKERİYA TÜFEKÇİ
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| Learning Outcomes of the Course |
: |
Learns the methods of improvement of the speech signal.
Has depth knowledge on Speech recognition
Obtains information about the methods of noise robust speech recognition
Learns information about the speaker adaptation
Has information about the speaker recognition
Has information about text to speech
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| Mode of Delivery |
: |
Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
The goal of this course is to develop a working knowledge of speech recognition,speaker recognition, and text to speech
including theory and practice. |
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| Course Contents |
: |
Speech enhancement, feature extraction, the EM algorithm, acoustic modeling, language modeling, training algorithms, search algorithms, noise robustness, speaker adaptation, speaker recognition, and text to speech. |
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| Language of Instruction |
: |
English |
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| Work Place |
: |
Classroom 2 |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Speech Enhancement |
Reading |
Lecture |
|
2 |
Feature Extraction |
Reading, homework |
Lecture |
|
3 |
The EM Algorithm |
Reading |
Lecture |
|
4 |
Acoustic Modeling |
Reading, homework |
Lecture |
|
5 |
Language Modeling |
Reading |
Lecture |
|
6 |
Training Algorithms |
Reading, homework |
Lecture |
|
7 |
Search Algorithms |
Reading |
Lecture |
|
8 |
Midterm exam |
Reading |
Written exam |
|
9 |
Noise Robustness |
Reading, homework |
Lecture |
|
10 |
Noise Robustness |
Reading |
Lecture |
|
11 |
Speaker Adaptation |
Reading, homework |
Lecture |
|
12 |
Speaker Adaptation |
Reading |
Lecture |
|
13 |
Speaker Recognition |
Reading, homework |
Lecture |
|
14 |
Speaker Recognition |
Reading |
Lecture |
|
15 |
Text to Speech |
Reading, homework |
Lecture |
|
16/17 |
Final exam |
Reading |
Written exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Spoken Language Processing. A Guide to Theory, Algorithm, and System Development. Xuedong Huang, Alex Acero, Hsiao-Wuen Hon
Discrete-Time Processing of Speech Signals. John R. Deller, John G. Proakis, John H. L. Hansen
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| |
| Required Course Material(s) | |
|
|
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
50 |
|
Homeworks/Projects/Others |
7 |
50 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
|
Final Assessments
|
100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Reaches wide and deep knowledge through scientific research in the field of computer engineering, evaluates, implements, and comments. |
4 |
|
2 |
Describes and uses information hidden in limited or missing data in the field of computer engineering by using scientific methods and integrates it with information from various disciplines. |
4 |
|
3 |
Follows new and emerging applications of computer engineering profession, if necessary, examines and learns them |
3 |
|
4 |
Develops methods and applies innovative approaches in order to formulate and solve problems in computer engineering. |
3 |
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5 |
Proposes new and/or original ideas and methods in the field of computer engineering in developing innovative solutions for designing systems, components or processes. |
3 |
|
6 |
Designs and implements analytical modeling and experimental research and solves the complex situations encountered in this process in the field of Computer Engineering |
3 |
|
7 |
works in multi disciplinary teams and takes a leading role and responsibility. |
1 |
|
8 |
Learns at least one foreign language at the European Language Portfolio B2 level to communicate orally and written |
3 |
|
9 |
Presents his/her research findings systematically and clearly in oral and written forms in national and international meetings. |
1 |
|
10 |
Describes social and environmental implications of engineering practice. |
2 |
|
11 |
Considers social, scientific and ethical values in collection, interpretation and announcement of data. |
3 |
|
12 |
Acquires a comprehensive knowledge about methods and tools of computer engineering and their limitations. |
4 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
|
Out of Class Study (Preliminary Work, Practice) |
14 |
4 |
56 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
7 |
5 |
35 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
10 |
10 |
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Total Workload: | 153 |
| Total Workload / 25 (h): | 6.12 |
| ECTS Credit: | 6 |
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