Automatic Speech Recognition (P01456)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-ASR

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 : Speech Processing (PPLS course) For Informatics PG students only, or by special permission of the School.

Subject Areas

Home subject area

Speech Processing, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
08/01/2008	12:10	13:00			To Be Confirmed

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	12:10	13:00	Central
Lecture	Friday	12:10	13:00	Central

Summary of Intended Learning Outcomes

It is anticipated that students who successfully complete the course will be able to:
* describe the statistical framework used for automatic speech recognition;
* understand the weakness of the simplified speech recognition systems and demonstrate knowledge of more advanced methods to overcome these problems;
* describe speech recognition as an optimization problem in probabilistic terms;
* relate individual terms in the mathematical framework for speech recognition to particular modules of the system;
* to build a large vocabulary continuous speech recognition system, using a standard software toolkit.