Postgraduate Course: Automatic Speech Recognition (INFR11033)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Credits | 10 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://course.inf.ed.ac.uk/asr |
Taught in Gaelic? | No |
| Course description | This 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)
|
Co-requisites | |
| 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
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
|
| Delivery period: 2013/14 Semester 2, Available to all students (SV1)
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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 |
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| 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
Assessment
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
| None |
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).
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| Study Abroad |
Not entered |
| Study Pattern |
Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 30
Private Study/Other 50
Total 100 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Mary Cryan
Tel: (0131 6)50 5153
Email: mcryan@inf.ed.ac.uk |
Course secretary | Miss Kate Farrow
Tel: (0131 6)50 2706
Email: Kate.Farrow@ed.ac.uk |
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© Copyright 2013 The University of Edinburgh - 13 January 2014 4:28 am
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