Statistical modeling and analysis of audio-visual association in speech

Currently, most dialog systems are restricted to single user environments. This thesis aims to promote an un-tethered multi-person dialog system by exploring approaches to help solve the speech correspondence problem (i.e. who, if anyone, is currently speaking). We adopt a statistical framework in which this problem is put in the form of a hypothesis test and focus on the subtask of discriminating between associated and non-associated audio-visual observations. Various methods for modeling our audio-visual observations and ways of carrying out this test are studied and their relative performance is compared. We discuss issues that arise from the inherently high dimensional nature of audio-visual data and address these issues by exploring different techniques for finding low-dimensional informative subspaces in which we can perform our hypothesis tests. We study our ability to learn a person-specific as well as a generic model for measuring audio-visual association and evaluate performance oil multiple subjects taken from MIT's AVTIMIT database.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2005

Author dc:creator

• Siracusa, Michael Richard, 1980-

Advisor dc:contributor.advisor

• Trevor Darrell and John W. Fisher.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/30182

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/30182