[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-424604-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-424604-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","evaluating-the-accuracy-of-automatic-speech-recognition-systems-in-home-healthcare-settings","EVALUATING THE ACCURACY OF AUTOMATIC SPEECH RECOGNITION SYSTEMS IN HOME HEALTHCARE SETTINGS","","Evaluating the accuracy of automatic speech recognition (ASR) systems for home healthcare communication. The study assessed AWS General Transcribe (AWS-GT) by rating audio clarity and comparing word error rate (WER) against gold-standard manual transcription. Analysis covered 4,002 utterances from clinician recordings and research assistants. Results showed statistically significant WER differences by recording type, with the highest errors for short utterances that improved as utterances lengthened. Findings support clinical adoption decisions for ASR-dependent documentation and prediction.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/evaluating-the-accuracy-of-automatic-speech-recognition-systems-in-home-healthcare-settings/424604/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/evaluating-the-accuracy-of-automatic-speech-recognition-systems-in-home-healthcare-settings/424604.png","ImageObject",300,407,{"name":42,"@type":43},"Rainbow Cat","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-04","2026-09-28",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What ASR system was evaluated in the study?","Question",{"text":63,"@type":64},"The study evaluated AWS General Transcribe (AWS-GT) for automated transcription of home healthcare audio recordings.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How was accuracy measured and compared?",{"text":68,"@type":64},"Accuracy was measured using word error rate (WER) against a gold-standard manual transcription.",{"name":70,"@type":61,"acceptedAnswer":71},"What utterances produced the poorest ASR performance?",{"text":72,"@type":64},"WER was poorest for short utterances of four to eight words, and performance improved as utterances became longer.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},424604,1791116184,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,105,109,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":107,"slug":108},7,40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":106,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":22,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":30,"preview_page_count":4},962090769181,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","1020 Innovation in Aging, 2025, Vol. 9, No. S2  \nassistants (RAs). Study staff rated the clarity of the audio using a scale of low, medium and high. The audio quality was similar among the two types of recordings. Our results are a part of a larger study aiming to use automated speech processing to identify risk factors for hospitalization and emergency department visits. Overall, 4002 utterances, defined as uninterrupted blocks of speech with four or more words, were analyzed— 3,472 (87%) from clinician recordings and 520 (13%) from RA recordings. Word error rate (WER; range 0(best)-1(worst)) was used to assess the performance of AWS-GT, compared to a gold-standard manual transcription. We found a statistically significant difference in WER for clinician recordings (.26) and RA recordings (.19) (p \u003C 0.05). We also observed that the WER was poorest for short, four-to-eight-words, utterances (.39), and that this improved as utterances grew longer (p \u003C 0.05) . Clinician recordings (35%) had a greater number of short utterances than RA recordings (25%) (p \u003C 0.05) . This shows how differences in conversation dynamics can impact the accuracy of ASR systems. It is incredibly important that we determine whether existing software is accurate enough for use in clinical documentation and prediction models dependent on ASR systems.  \nAbstract citation ID: igaf122.3438  \nEVALUATING THE ACCURACY OF AUTOMATIC  \nSPEECH RECOGNITION SYSTEMS IN HOME  \nHEALTHCARE SETTINGS  \nDayoung Yu1, Sasha Vergez2, Grace Flaherty1, Maryam Zolnoori3, Nicole Onorato1, Julia Hirschberg3, Maxim Topaz3, and Margaret McDonald1, 1. VNS Health, New York, New York, United States, 2. VNS Health, Bronx, New York, United States, 3. Columbia University, New York, New York, United States  \nRecent developments in automatic speech recognition (ASR) systems have resulted in increased use of automated transcription services to analyze verbal communications. We aimed to analyze the accuracy of AWS General Transcribe (AWS-GT) on audio recordings of home healthcare patients. These audio recordings included in-person clinician visits and phone calls with research","cbCaimPIT762VJet","https://ap.wps.com/l/cbCaimPIT762VJet","pdf",47687,"English","# Purpose and Methods\n## Audio and Utterance Sampling\n## Performance Metric: WER\n# Results\n## WER Differences by Recording Type\n## Effects of Utterance Length\n# Implications","[{\"question\":\"What ASR system was evaluated in the study?\",\"answer\":\"The study evaluated AWS General Transcribe (AWS-GT) for automated transcription of home healthcare audio recordings.\"},{\"question\":\"How was accuracy measured and compared?\",\"answer\":\"Accuracy was measured using word error rate (WER) against a gold-standard manual transcription.\"},{\"question\":\"What utterances produced the poorest ASR performance?\",\"answer\":\"WER was poorest for short utterances of four to eight words, and performance improved as utterances became longer.\"}]","EVALUATING THE ACCURACY OF AUTOMATIC SPEECH RECOGNITION SYSTEMS IN HOME HEALTHCARE SETTINGS | PDF",1790634742]