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dc.contributor.advisorGraham M. Jones.en_US
dc.contributor.authorSemel, Beth Michelle.en_US
dc.contributor.otherMassachusetts Institute of Technology. Program in Science, Technology and Society.en_US
dc.date.accessioned2020-03-09T18:51:46Z
dc.date.available2020-03-09T18:51:46Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/124064
dc.descriptionThesis: Ph. D. in History, Anthropology, and Science, Technology and Society (HASTS), Massachusetts Institute of Technology, Program in Science, Technology and Society, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractThis 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.en_US
dc.description.abstractBy turning to the expertise of computer scientists and engineers, they seek to build "machine listening" prototypes for psychiatric assessment: technologies that use a microphone to capture sound and artificial intelligence (AI) to analyze sound. While their studies are premised on the notion that AI can listen beyond the human by attending to sounds of speech that have psychopathological significance supposedly set aside from linguistic meaning and human difference, in order to gather and classify the data necessary for building their technologies, researchers must rely on the very components of language that they seek to overcome: its interactional, sociocultural dimensions. I show how the connections between spoken utterances and inner states that researchers design their systems to make "autonomously" depend upon a tightly managed but oftentimes hidden infrastructure of human labor, including the labor of research subjects.en_US
dc.description.abstractThe division of labor within the teams replicates hierarchies of value within mental health care professions, which place diagnosis and treatment at the top as expert, biomedically and legally ratified forms of judgment, and place the data entry and triage work of assessment at the bottom, as skilless, para-professional, and mechanized tasks. In describing the vexed status and ethics of listening, language, labor, and care in contemporary U.S. mental health care, the dissertation tells a larger story about the stakes of framing mental illness as a scientific, bureaucratic problem calling for a technological interventionen_US
dc.description.statementofresponsibilityby Beth Michelle Semel.en_US
dc.format.extent341 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectProgram in Science, Technology and Society.en_US
dc.titleSpeech, signal, symptom : machine listening and the remaking of psychiatric assessmenten_US
dc.typeThesisen_US
dc.description.degreePh. D. in History, Anthropology, and Science, Technology and Society (HASTS)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Program in Science, Technology and Societyen_US
dc.identifier.oclc1142178093en_US
dc.description.collectionPh.D.inHistory,Anthropology,andScience,TechnologyandSociety(HASTS) Massachusetts Institute of Technology, Program in Science, Technology and Societyen_US
dspace.imported2020-03-09T18:51:46Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentSTSen_US


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