HUT - CIS /Opinnot/T-61.182/2003/kurssiesite2003.shtml

T-61.182 Robustness in Language and Speech Processing
Special course on Information Technology II (4 ov, L)

Lecturer: Prof. Mikko Kurimo
Assistant: DI Teemu Hirsimäki
Semester: Spring 2003
Credit points: 2-4 cr
Place: Seminar room T4
Time: Thursdays 14-16, starting from February 6
Language: English (or Finnish)
Homepage: http://www.cis.hut.fi/Opinnot/T-61.182/

Seminar course description

As speech and language technology innovations enter the everyday use, a research issue with growing importance is to develop methods that are robust to different forms of noise and changes in their using environment. Although some automatic speech recognition and natural language understanding systems do already a pretty good job in certain well-defined applications and conditions, their performance fails dramatically compared human speech and language processing abilities when the conditions change abruptly or the input is somehow disturbed. The problem in estimation of statistical models and learning representations from data is that it is difficult to collect enough training data to be able to properly model even the most likely types of noise and conditions. For adaptive methods the challenge is to make as general adaptations as possible using only a very limited amount of available adaptation data.

This course addresses robustness issues at the speech recognition and natural language parsing levels, with a focus on feature extraction and noise robust recognition, adaptive systems, language modeling, parsing, and natural language understanding. The purpose is to give a clear overview of the main technologies used in language and speech processing and discuss their robustness. It also brings together speech and language technologies often considered separately.

The course is based on the recent book: Robustness in Language and Speech Processing, edited by Jean-Claude Junqua and Gertjan van Noord, Kluwer Academic Publishers (2001).

Prerequisites

The basic knowledge of pattern recognition, speech processing, speech recognition, and natural language processing methods is helpful.

Requirements for passing the course

NOTE: To cover well this broad topic we decided that each participant should give two presentations. To balance the work load there will be no home exercises.

To pass the course (4cr), you now have to

participate actively,
give two seminar talks on book chapters or articles selected for the course
carry out a small project work, that is, a small-scale research project or a literature study on a topic related to the course.

To pass with distinction, the seminar talk, the handouts, and the project work must each be very good. Without the project work the course can be completed to obtain two credits.

Relationship to other studies

At TKK the course is suited for the Language Technology major (Kieliteknologian pää/sivuaine) and for studies in Information Technology.

Also students and staff from the KIT (Kieliteknologian opetuksen verkosto) are welcome.

More information

Mikko.Kurimo@hut.fi (tel. 451 5388)