THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2011/2012

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Informatics : Informatics

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 (Postgraduate) Credits 10
Home subject area Informatics Other subject area None
Course website https://www.inf.ed.ac.uk/teaching/courses/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
Pre-requisites It is RECOMMENDED that students have passed Speech Processing (LASC11065)
Co-requisites
Prohibited Combinations Other requirements For Informatics PG students and final year MInf students only, or by special permission of the School.
Additional Costs None
Information for Visiting Students
Pre-requisites None
Displayed in Visiting Students Prospectus? Yes
Course Delivery Information
Delivery period: 2011/12 Semester 2, Available to all students (SV1) WebCT enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 15:00 - 15:50
CentralLecture1-11 15:00 - 15:50
First Class First class information not currently available
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).
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 Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk
Course secretary Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk
Navigation
Help & Information
Home
Introduction
Glossary
Search DPTs and Courses
Regulations
Regulations
Degree Programmes
Introduction
Browse DPTs
Courses
Introduction
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Timetab
Prospectuses
Important Information
 
copyright 2011 The University of Edinburgh - 3 April 2011 11:21 am