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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Automatic Speech Recognition (UG) (INFR11219)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis course follows the delivery and assessment of Automatic Speech Recognition (INFR11033) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11033 instead.
Course description This course follows the delivery and assessment of Automatic Speech Recognition (INFR11033) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11033 instead.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites It is RECOMMENDED that students have passed Speech Processing (Hons) (LASC10061)
Co-requisites
Prohibited Combinations Students MUST NOT also be taking Automatic Speech Recognition (INFR11033)
Other requirements This course follows the delivery and assessment of Automatic Speech Recognition (INFR11033) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11033 instead.

This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.

Some general mathematical ability is essential; Special functions log, exp are fundamental; mathematical notation (such as sums) used throughout; some calculus. Probability theory is used extensively: joint and conditional probabilities, Gaussian and multinomial distributions.

Programming using Python or shell scripting is required for the practicals and coursework.
Information for Visiting Students
Pre-requisitesAs above
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 15, Seminar/Tutorial Hours 8, Supervised Practical/Workshop/Studio Hours 5, Feedback/Feedforward Hours 6, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 62 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) Exam 50%
Coursework 50%

Assessed coursework will be worth 50% of the grade of the course. This will consist of:
- 5 short weekly practical assignments (1-2 hours each) worth 10% in total;
- A longer practical and written assignment (expected to take around 30 hours work) worth 40%.

Both sets of coursework will use Python and other standard software toolkits to develop a speech recognition system. They will be marked in compliance with the Common Marking Scheme.
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)Automatic Speech Recognition (UG) (INFR11219)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Describe the statistical framework used for automatic speech recognition
  2. Understand the weakness of the simplified speech recognition systems and demonstrate knowledge of more advanced methods to overcome these problems
  3. Describe speech recognition as an optimization problem in probabilistic terms
  4. Relate individual terms in the mathematical framework for speech recognition to particular modules of the system
  5. Build a large vocabulary continuous speech recognition system, using a standard software toolkit
Reading List
John N. Holmes, Wendy J. Holmes, "Speech Synthesis and Recognition", Taylor & Francis (2001), 2nd edition
Xuedong Huang, Alex Acero and Hsiao-Wuen Hon, "Spoken language processing: a guide to theory, algorithm, and system development", Prentice Hall (2001)
Lawrence R. Rabiner and Biing-Hwang Juang, "Fundamental of Speech Recognition", Prentice Hall (1993)
B. Gold, N. Morgan, "Speech and Audio Signal Processing: Processing and Perception of Speech and Music", John Wiley and Sons (1999)
Additional Information
Graduate Attributes and Skills Not entered
Keywordstheory,automatic speech recognition,artificial intelligence,natural language computing
Contacts
Course organiserDr Peter Bell
Tel: (0131 6)51 3284
Email: peter.bell@ed.ac.uk
Course secretaryMrs Helen Tweedale
Tel: (0131 6)50 3827
Email: Helen.Tweedale@ed.ac.uk
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