{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T08:03:06Z","timestamp":1790236986713,"version":"4.1.0"},"posted":{"date-parts":[[2026,9,21]]},"group-title":"Otolaryngology","reference-count":17,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"award":["OT2-OD032720-01S3"],"award-info":[{"award-number":["OT2-OD032720-01S3"]}],"id":[{"id":"https:\/\/ror.org\/001d55x84","id-type":"ROR","asserted-by":"publisher"}]},{"id":[{"id":"https:\/\/ror.org\/032db5x82","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,21]]},"abstract":"<jats:label>1.<\/jats:label>\n                <jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>Conventional clinical acoustic analysis systems are confined to specialized clinical environments, which limits scalable and remote voice monitoring. This study aimed to validate LIS-N, a web-based prototype of a novel mobile application that uses the open-source Praat-Parselmouth library for acoustic feature extraction, against the Computerized Speech Lab (CSL), the clinical reference standard, to assess its potential for telehealth and longitudinal voice assessment before integration into clinical or research workflows.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Method<\/jats:title>\n                  <jats:p>Twenty adult volunteers without voice complaints or diagnosed dysphonia completed three voice tasks across three consecutive days at a tertiary academic voice center in quiet isolated rooms. All participants completed the full protocol across three consecutive days. Voice tasks included Rainbow Passage reading, sustained vowel \/i\/, and maximum phonation time \/a\/. Audio was captured concurrently using CSL and the LIS-N prototype, which uses Praat-Parselmouth for acoustic feature extraction. To isolate the contributions of recording hardware and analysis software, four cross-system conditions were also examined. Agreement was assessed using Pearson correlation and Bland-Altman analyses. Session-to-session trajectory correlations were computed to evaluate temporal consistency across systems.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Fundamental frequency and Pitch Mean demonstrated excellent agreement across all tasks and conditions (r &gt; 0.98), with robustness to hardware and software differences. Pitch Minimum and Pitch Maximum showed strong agreement during sustained vowel tasks. Energy-based features showed moderate agreement, with variability driven primarily by microphone differences, and same-microphone conditions yielded substantially stronger energy agreement. Perturbation measures including jitter, shimmer and CPP showed consistently poor cross-platform agreement, consistent with prior literature.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>This study provides proof of concept for the viability of open-source acoustic frameworks as accessible alternatives to proprietary clinical systems, while also identifying limitations. Both systems should be interpreted separately, as each remains reliable within its own framework. The LIS-N prototype demonstrated strong agreement for frequency-based voice measures and consistent longitudinal monitoring, supporting adoption of open-source acoustic tools in telehealth and remote voice monitoring.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.09.14.26362731","type":"posted-content","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T18:05:10Z","timestamp":1790013910000},"source":"Crossref","is-referenced-by-count":0,"title":["A Comparative Study of Acoustic Feature Extraction Using CSL and Web Prototype of LIS-N Application"],"prefix":"10.64898","author":[{"given":"Shrramana Ganesh","family":"Sudhakar","sequence":"first","affiliation":[{"name":"University of South Florida, Tampa, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabrina","family":"Musteric","sequence":"additional","affiliation":[{"name":"University of South Florida, Tampa, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephanie","family":"Watts","sequence":"additional","affiliation":[{"name":"University of South Florida, Tampa, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Ebraheem","sequence":"additional","affiliation":[{"name":"University of South Florida, Tampa, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John Michael","family":"Templeton","sequence":"additional","affiliation":[{"name":"University of South Florida, Tampa, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"Bridge2AI-Voice Consortium","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya\u00ebl","family":"Bensoussan","sequence":"additional","affiliation":[{"name":"University of South Florida, Tampa, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"issue":"4","key":"2026092400550538000_2026.09.14.26362731v1.1","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1001\/jamaoto.2023.4807","article-title":"Voice as an AI biomarker of health\u2014 introducing audiomics","volume":"150","year":"2024","journal-title":"JAMA Otolaryngology\u2013Head & Neck Surgery"},{"key":"2026092400550538000_2026.09.14.26362731v1.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wocn.2018.07.001"},{"key":"2026092400550538000_2026.09.14.26362731v1.3","doi-asserted-by":"crossref","unstructured":"Hirano, M. , & McCormick, K. R . (1986). Clinical examination of voice by Minoru Hirano.","DOI":"10.1121\/1.393788"},{"key":"2026092400550538000_2026.09.14.26362731v1.4","doi-asserted-by":"publisher","DOI":"10.1159\/000266220"},{"issue":"2","key":"2026092400550538000_2026.09.14.26362731v1.5","first-page":"87","article-title":"Two Cases Using the Praat-Based Automatic Voice Analysis Program as an Alternative to CSL. Journal of The Korean Society of Laryngology","volume":"32","year":"2021","journal-title":"Phoniatrics and Logopedics"},{"key":"2026092400550538000_2026.09.14.26362731v1.6","first-page":"7","article-title":"The worldwide impact of telemedicine during COVID-19: current evidence and recommendations for the future","volume":"1","year":"2022","journal-title":"Connected health"},{"key":"2026092400550538000_2026.09.14.26362731v1.7","unstructured":"Boersma, Paul & Weenink, David (2026). Praat: doing phonetics by computer [Computer program]. 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N. , Bahr, R. , Watts, S. , Boyer, M. , Budinsky, R. , Bensoussan, Y. , & Bridge2AI-Voice Consortium. (2024). Evidence-based recommendations for tablet recordings from the Bridge2AI-voice acoustic experiments. 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Comparing praat and snack formant measurements on two large corpora of northern and southern French. In INTERSPEECH (pp. 1006-1009).","DOI":"10.21437\/Interspeech.2007-354"},{"issue":"1","key":"2026092400550538000_2026.09.14.26362731v1.16","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1044\/1092-4388(2013\/12-0103)","article-title":"Quantitative and descriptive comparison of four acoustic analysis systems: Vowel measurements","volume":"57","year":"2014","journal-title":"Journal of Speech, Language, and Hearing Research"},{"key":"2026092400550538000_2026.09.14.26362731v1.17","unstructured":"Streamlit. (2025). Streamlit \u2022 The fastest way to build and share data apps. 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