The emergency transcriber: a situation-aware recording system for noisy acoustic environments

Gao, Yunlong

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https://hdl.handle.net/2142/50632 Copy

Description

Title

The emergency transcriber: a situation-aware recording system for noisy acoustic environments

Author(s)

Gao, Yunlong

Issue Date

2014-09-16

Director of Research (if dissertation) or Advisor (if thesis)

Abdelzaher, Tarek F.

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Cyber-physical System
• Sensing Classification
• Speech Recognition
• Situation Awareness
• Emergency Personnel

Abstract

The thesis presents a novel situation awareness tool for sensing classification. We proposed a general scheme for sensing, and applied that to build an acoustic tool for teams of first responders and emergency personnel. It constitutes an audio interface for reliably recording and disseminating situation progress as extracted from the team’s audio communications. The tool that we built is intended for emergency teams operating in noisy acoustic environments, where standalone speech recognition systems fail to deliver desired accuracy. Such teams typically follow predefined collaborative workflow as dictated by the relevant engagement protocols, specifying their roles and communications. Given the critical nature of the situation, the vocabulary used is often constrained and dependent on the current stage of the workflow being executed. Treating a traditional speech recognition component as a noisy sensor, the novelty of our tool lies in exploiting knowledge of the workflow to correct the noisy measurements. The intellectual contribution in this exploitation lies in the joint estimation of the current state of the workflow together with the correction of sensed data, given only the noisy (speech) measurements and an overall workflow description. Evaluation shows that the tool provides a significant accuracy enhancement compared to the standalone speech recognition, effectively coping with the noisy environment of emergency teams.

Graduation Semester

2014-08

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http://hdl.handle.net/2142/50632

Copyright and License Information

Copyright 2014 Yunlong Gao

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