Speech, signal, symptom : machine listening and the remaking of psychiatric assessment

This multi-sited, ethnographic dissertation follows teams of psychiatric and engineering professionals collaborating to tackle one of Western psychiatry's longest standing issues: the subjective nature of mental illness. Situated at three different U.S.-based universities, the teams are driven by a conviction that conventional methods of psychiatric screening are fallible if not altogether inaccurate, since they depend upon a mental health care worker's ability to interpret the semantic content of a patient's speech. Through research studies involving human subjects, the teams hope to develop more biologically based and resource-efficient screening techniques that instead analyze paralinguistic, acoustic components of speech-such as pitch, speaking rate, and breathiness-which they argue are more directly linked to the internal mechanisms that drive mental illness.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Program in Science, Technology and Society

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Semel, Beth Michelle.

Advisor dc:contributor.advisor

• Graham M. Jones.

• Program in Science, Technology and Society.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124064

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124064