Postgraduate Course: Automatic Speech Recognition (INFR11033)
|School||School of Informatics
||College||College of Science and Engineering
||Availability||Available to all students
|Credit level (Normal year taken)||SCQF Level 11 (Year 4 Undergraduate)
|Home subject area||Informatics
||Other subject area||None
||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)
|| It is RECOMMENDED that students have passed
Speech Processing (LASC11065)
||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
|Course Start Date
|Breakdown of Learning and Teaching activities (Further Info)
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
|Breakdown of Assessment Methods (Further Info)
||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.
|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.
||* 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)
* 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
||* 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).
Timetabled Laboratories 0
Non-timetabled assessed assignments 30
Private Study/Other 50
|Course organiser||Dr Mary Cryan
Tel: (0131 6)50 5153
|Course secretary||Miss Kate Farrow
Tel: (0131 6)50 2706
© Copyright 2013 The University of Edinburgh - 13 January 2014 4:28 am