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Effective communication is vital in emergency response scenarios where clarity and speed can save lives. Traditional systems often struggle under the chaotic conditions of real-world emergencies, leading to breakdowns in communication and task management. This paper introduces CLEAR-Command, a system that leverages Large Language Models (LLMs) to enhance emergency communications. CLEAR stands for textbfCoordinatedListening,Extraction,andAllocationinResponse.CLEAR−Commandautomatesthetranscription,summarization,andtaskextractionfromliveradiocommunicationsofemergencyfirstrespondersusingtheOpenAIWhisperAPIfortranscriptionandgpt−4oforsummarizationandtaskextraction.Oursystemprovidesadynamicoverviewoftaskallocationsandtheirexecutionstatus,significantlyimprovingtheaccuracyoftaskidentificationandtheclarityofcommunication.Weevaluatedoursystemthroughanexpertpre−studywith4expertsandauserstudywith13participants.Theexpertpre−studyidentifiedgpt−4oasprovidingthemostaccuratetaskextraction,whiletheuserstudyshowedthatCLEAR−Commandsignificantlyoutperformstraditionalradiocommunicationintermsofclarity,trust,andcorrectnessoftaskextraction.Ourdemoishostedunderthislink,andallprojectdetailsarepresentedinourGitlabpage.