Learning Internet from tone of voice

The ability to discern information from the tone of voice that a person uses is an important part of social interactions. Synthetic characters that can interact naturally with humans could take advantage of this information if they could discern it. I propose that a synthetic character with a vocalization affect classifier and the ability to learn associations can use the tone of voice of the person interacting with her to predict what the person is going to do. In this approach the classifier learns to distinguish tones in real time allowing the character to adapt to new tones. I describe the implementation of the system, called Minimus T.O. Mouse, and its extensions from previous affect classifying systems and previous synthetic characters.

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

2004

Author dc:creator

• Cochran, Jennie E. (Jennie Eleanor), 1981-

Advisor dc:contributor.advisor

• Bruce Blumberg.

• 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

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/28383

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/28383