2026-10-10T16:55:19Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33949177 2026-09-21T04:30:19Z category_388 portal_316 item_type_3 month_year_09_2026

Table 2_The tongue may not move, but the voice will: preoperative AI voice cloning for identity preservation in major glossectomy — a prospective feasibility study.xlsx Prajwal S. Dange (25079959) Karthik N. Rao (24714661) Kumareshwar TV (25079962) Teertha Shetty (24714664) Radhika Kapahtia (25079965) Krishna Chaitanya (1779343) Sreeram MP (24714673) Oncology and Carcinogenesis not elsewhere classified major glossectomy multilingual speech synthesis speaker similarity tongue neoplasms voice banking Background<p>Major glossectomy frequently results in profound speech impairment. We investigated whether a patient’s 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.</p>Methods<p>In this prospective single-centre feasibility study, adult patients with treatment-naïve carcinoma of the tongue planned for major glossectomy underwent a single approximately 30–60 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’s 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.</p>Results<p>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 dB. 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.</p>Conclusion<p>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.</p> 2026-09-21T04:30:19Z Dataset Dataset 10.3389/fonc.2026.1915964 https://figshare.com/articles/dataset/Table_2_The_tongue_may_not_move_but_the_voice_will_preoperative_AI_voice_cloning_for_identity_preservation_in_major_glossectomy_a_prospective_feasibility_study_xlsx/33949177 CC BY 4.0