2026-09-19T13:50:47Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/768172022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Peter Szolovits.
Gür, Burkay
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2013-02-13T21:24:05Z
2013-02-13T21:24:05Z
2012
2012
http://hdl.handle.net/1721.1/76817
825763209
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 73-74).
Accurate and comprehensive data form the lifeblood of health care. Unfortunately, there is much evidence that current data collection methods sometimes fail. Our hypothesis is that it should be possible to improve the thoroughness and quality of information gathered through clinical encounters by developing a computer system that (a) listens to a conversation between a patient and a provider, (b) uses automatic speech recognition technology to transcribe that conversation to text, (c) applies natural language processing methods to extract the important clinical facts from the conversation, (d) presents this information in real time to the participants, permitting correction of errors in understanding, and (e) organizes those facts into an encounter note that could serve as a first draft of the note produces by the clinician. In this thesis, we present our attempts to measure the performances of two state-of-the-art automatic speech recognizers (ASRs) for the task of transcribing clinical conversations, and explore the potential ways of optimizing these software packages for the specific task. In the course of this thesis, we have (1) introduced a new method for quantitatively measuring the difference between two language models and showed that conversational and dictational speech have different underlying language models, (2) measured the perplexity of clinical conversations and dictations and shown that spontaneous speech has a higher perplexity than dictational speech, (3) improved speech recognition accuracy by language adaptation using a conversational corpus, and (4) introduced a fast and simple algorithm for cross talk elimination in two speaker settings.
by Burkay Gür.
M.Eng.
74 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Improving speech recognition accuracy for clinical conversations
Thesis
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<Abstract>Accurate and comprehensive data form the lifeblood of health care. Unfortunately, there is much evidence that current data collection methods sometimes fail. Our hypothesis is that it should be possible to improve the thoroughness and quality of information gathered through clinical encounters by developing a computer system that (a) listens to a conversation between a patient and a provider, (b) uses automatic speech recognition technology to transcribe that conversation to text, (c) applies natural language processing methods to extract the important clinical facts from the conversation, (d) presents this information in real time to the participants, permitting correction of errors in understanding, and (e) organizes those facts into an encounter note that could serve as a first draft of the note produces by the clinician. In this thesis, we present our attempts to measure the performances of two state-of-the-art automatic speech recognizers (ASRs) for the task of transcribing clinical conversations, and explore the potential ways of optimizing these software packages for the specific task. In the course of this thesis, we have (1) introduced a new method for quantitatively measuring the difference between two language models and showed that conversational and dictational speech have different underlying language models, (2) measured the perplexity of clinical conversations and dictations and shown that spontaneous speech has a higher perplexity than dictational speech, (3) improved speech recognition accuracy by language adaptation using a conversational corpus, and (4) introduced a fast and simple algorithm for cross talk elimination in two speaker settings.</Abstract>
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