{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T05:08:01Z","timestamp":1789967281448,"version":"4.0.1"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"\n Background<\/jats:title>\n Major glossectomy frequently results in profound speech impairment. We investigated whether a patient\u2019s voice could be captured preoperatively and reconstructed using artificial intelligence voice cloning for potential future use as a personalized assistive communication voice during postoperative recovery.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In this prospective single-centre feasibility study, adult patients with treatment-na\u00efve carcinoma of the tongue planned for major glossectomy underwent a single approximately 30\u201360 second preoperative voice recording in a sound-treated room. Voices were cloned offline using the IndicF5 flow-matching text-to-speech model, generating a standardized reading-passage output and a patient-transcript output for each participant. Speaker fidelity was assessed using cosine similarity across three independent speaker-verification encoders, calibrated against each patient\u2019s intra-speaker and cohort impostor distributions, alongside mel-cepstral distortion and prosodic feature analysis. Subjective acceptance was assessed using the Personalised Synthetic Voice Acceptance Questionnaire across six domains, completed by patients and family members.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Of 34 enrolled patients, 26 formed the final cohort across four languages. Mean cosine similarity was 0.933 (Resemblyzer), 0.969 (WavLM), and 0.806 (ECAPA-TDNN), with clone scores generally falling within patient-specific intra-speaker distributions and above cohort impostor distributions. Mean mel-cepstral distortion was 7.87\u00a0dB. Fundamental frequency and other prosodic measures showed broad cohort-level correspondence with variable individual deviations. Identity-and-Ownership and Authenticity-and-Trust domains scored highest among both patients and family or attenders, while naturalness and emotional acceptance were rated more conservatively.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n These findings demonstrate the feasibility of generating personalised synthetic voices from brief preoperative clinic-grade recordings, with objective speaker similarity and preliminary patient- and family-reported acceptance. The findings support further evaluation of these voices as potential assistive communication tools during postoperative recovery; however, postoperative communication performance and clinical benefit were not evaluated in the present study.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fonc.2026.1915964","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T04:29:02Z","timestamp":1789964942000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The tongue may not move, but the voice will: preoperative AI voice cloning for identity preservation in major glossectomy \u2014 a prospective feasibility study"],"prefix":"10.3389","volume":"16","author":[{"given":"Prajwal S.","family":"Dange","sequence":"first","affiliation":[{"name":"Department of Head and Neck Surgical Oncology, Sri Shankara Cancer Hospital and Research Centre","place":["Bengaluru, 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