Pietrowicz, Mary

Abstract Objective Effective communication with humans and automated voice assistants (AVA) is essential for full participation in modern life. Because AVA relies on intelligible speech, individuals with speech impairments, such as those treated for oral cavity cancer (OCC), may face barriers when interacting with AVA. We assessed self‐reported communication difficulty in patients treated for OCC related to AVA and humans. Study Design Prospective cross‐sectional cohort study. Setting Out‐patient clinics in two tertiary care centers. Methods This study was conducted between July 2024 and March 2025 and included adults (≥18 years) diagnosed with and surgically treated for OCC (experimental group) and those without speech impairments (controls). Participants completed the Speech Communication Experience Survey (SCES), which assessed perceived intelligibility when communicating with familiar partners, strangers, and AVA; and, as reported elsewhere, the Speech Handicap Index (SHI). Results In total, 159 participants were included (69 OCC, 90 controls). OCC participants were more likely to report that familiar partners, strangers, and AVA “almost always” or “always” had difficulty understanding their speech ( P < .05). Free flap reconstruction and maxillectomy were associated with greater reported communication difficulty. AVA usage frequency and overall opinions toward AVA did not significantly differ between groups. Conclusion Patients treated for OCC report significantly greater difficulty being understood by both AVA and human listeners compared to controls. These perceptions may inform peri‐operative counseling and guide the development of more inclusive AVA technologies capable of accommodating diverse speakers. Level of Evidence : 3.

This study evaluated the feasibility of differentiating conversational and clear speech produced by individuals with muscle tension dysphonia (MTD) using landmark-based analysis of speech (LMBAS). Thirty-four adult speakers with MTD recorded conversational and clear speech, with 27 of them able to produce clear speech. The recordings of these individuals were analyzed with the open-source LMBAS program, SpeechMark®, matlab Toolbox version 1.1.2. The results indicated that glottal landmarks, burst onset landmarks, and the duration between glottal landmarks differentiated conversational speech from clear speech. LMBAS shows potential as an approach for detecting the difference between conversational and clear speech in dysphonic individuals.