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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Automatic Speech Recognition (INFR11033)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) Credits10
Home subject areaInformatics Other subject areaNone
Course website Taught in Gaelic?No
Course descriptionThis course covers the theory and practice of automatic speech recognition (ASR), with a focus on the statistical approaches that comprise the state of the art. The course introduces the overall framework for speech recognition, including speech signal analysis, acoustic modelling using hidden Markov models, language modelling and recognition search. Advanced topics covered will include speaker adaptation, robust speech recognition and speaker identification. The practical side of the course will involve the development of a speech recognition system using a speech recognition software toolkit.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites It is RECOMMENDED that students have passed Speech Processing (LASC11065)
Prohibited Combinations Other requirements 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 (at pre-honours Maths for Informatics level) 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.
Additional Costs None
Information for Visiting Students
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2013/14 Semester 2, Available to all students (SV1) Learn enabled:  No Quota:  None
Web Timetable Web Timetable
Course Start Date 13/01/2014
Breakdown of Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 15, 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 70 )
Additional Notes
Breakdown of Assessment Methods (Further Info) Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Summary of Intended Learning Outcomes
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 - to build a large vocabulary continuous speech recognition system, using a standard software toolkit.
Assessment Information
Written Examination 70
Assessed Assignments 30
Oral Presentations 0

Assessed coursework will comprise the development of a speech recognition system using a standard software toolkit.

If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Special Arrangements
Additional Information
Academic description Not entered
Syllabus * Signal analysis for ASR
* Statistical pattern recognition (Bayes decision theory, Learning algorithms, Evaluation methods, Gaussian mixture model, and EM algorithm)
* Hidden Markov Models (HMM)
* Context-dependent models
* Discriminative training
* Language models for LVCSR (large vocabulary continuous speech recognition)
* Decoding
* Robust ASR (Robust features Noise reduction, Microphone arrays)
* Adaptation (Noise adaptation, Speaker adaptation/normalization, Language model adaptation)
* Speaker recognition
* History of speech recognition
* Advanced topics (Using prosody for ASR, Audio-visual ASR, Indexing, Bayesian network)

Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Natural Language Computing
Transferable skills Not entered
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).
Study Abroad Not entered
Study Pattern Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 30
Private Study/Other 50
Total 100
KeywordsNot entered
Course organiserDr Mary Cryan
Tel: (0131 6)50 5153
Course secretaryMiss Kate Farrow
Tel: (0131 6)50 2706
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