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.

Pietrowicz et al. (2022) demonstrated feasibility of detecting potential speech-based biomarkers to identify gastroesophageal reflux disease (GERD) and Barrett’s esophagus (BE); however, speech samples were obtained using research-grade recording equipment (TASCAM DR-40X with AKG C555L microphone). The use of more readily available equipment such as the Apple iPad with a native microphone could simplify data collection and allow access to a broader range of participants. Therefore, this study compared the research-grade recordings to iPad recordings for feasibility of detecting GERD and BE. One-hundred-fifty adults, clinically classified as GERD negative or GERD positive with or without BE, recorded a paragraph (The Rainbow Passage) simultaneously on both a TASCAM and iPad. Both data sets were analyzed manually for Alpha Ratio and Cepstral Peak Prominence (CPP) with Praat. Initial results show significantly higher alpha ratio and lower CPP in iPad recordings, indicating greater noise energy in the high-frequency range. Additionally, the capability of machine and deep learning model types to discern across GERD negative, GERD positive, BE, and normal voices were compared. Detection across these conditions and comparison of acoustic findings were explored to determine usefulness of the less controlled option of recording using an iPad with its internal microphone.

This study explores the feasibility of detecting gastroesophageal reflux disease (GERD) and Barrett’s esophagus (BE) using acoustic features extracted from speech recordings. GERD is a common condition affecting up to 27.8% of adults in the US. Chronic GERD is associated with BE, the precursor to esophageal adenocarcinoma. An automatic screening tool for GERD would improve clinical outcomes by improving early detection. Biomarkers based on acoustic features of speech may hold promise as hoarseness often co-occurs with GERD. The study cohort consisted of 49 adults with and 63 without GERD based on esophagogastroduodenoscopy or ambulatory pH studies. Speech, including sustained vowels, sentences, and reading, was systematically recorded. Voice quality was validated through auditory-perceptualratings with three speech-language pathologists. Automated, cross-validated, search across multiple machine and deep learning model types was performed using the recorded speech. Detection of condition presence and severity are explored, and model performance is reported, along with an assessment of the ability of acoustic features to discern across conditions.