Impact of naturalistic smartphone positioning on acoustic measures of voicea).

Smartphone technology has been used for at-home health monitoring, but there are few available applications (apps) for tracking acoustic measures of voice for those with chronic voice problems. Current apps limit the user by restricting the range of smartphone positions to those that are unnatural and non-interactive. Therefore, we aimed to understand how more natural smartphone positions impacted the accuracy of acoustic measures in comparison to clinically acquired and derived measures. Fifty-six adults (11 vocally healthy, 45 voice disordered, aged 18-80 years) completed voice recordings while holding their smartphones in four different positions (e.g., as if reading from the phone, up to the ear, etc.) while a head-mounted high-quality microphone attached to a handheld acoustic recorder simultaneously captured voice recordings. Comparisons revealed that mean fundamental frequency (Hz), maximum phonation time (s), and cepstral peak prominence (CPP; dB) were not impacted by phone position; however, CPP was significantly lower on smartphone recordings than handheld recordings. Spectral measures (low-to-high spectral ratio, harmonics-to-noise ratio) were impacted by the phone position and the recording device. These results indicate that more natural phone positions can be used to capture specific voice measures, but not all are directly comparable to clinically derived values.

Factors Related to Treatment Attendance for Patients Seeking Gender-Affirming Voice Therapy.

OBJECTIVE: The purpose of this study was to investigate factors related to treatment attendance for patients seeking gender-affirming voice therapy (GAVT). METHOD: We completed retrospective chart reviews of 50 patients (43 transgender women, three transgender men, four nonbinary patients; aged 18-67 years, M = 34.92 years, SD = 12.32 years) referred for GAVT at a Midwest outpatient center from 2016 to 2021. Data extraction included patient demographics, therapy visit specifics (e.g., number of sessions attended, treatment completion status, and in-person vs. virtual visits) and treatment timing (in relation to the onset of the COVID-19 pandemic), and psychosocial and socioeconomic information. RESULTS: There was no impact of treatment-timing relative to the onset of the pandemic on any attendance measure; however, patients were 1.9 times more likely to complete therapy with each virtual telehealth session attended. Individual factors of outside social support and hormone replacement therapy were positively related to the number of therapy sessions attended, whereas insurance provider and employment status were related to therapy completion. CONCLUSIONS: Telehealth attendance during the COVID-19 pandemic was positively associated with GAVT completion. Future research should investigate psychosocial and socioeconomic factors to understand how to identify patients at risk for poor treatment adherence and facilitate access to clinical care.

Comparison Between Custom Smartphone Acoustic Processing Algorithms and Praat in Healthy and Disordered Voices.

OBJECTIVE: The aim of this study was to understand the relationship between temporal and spectral-based acoustic measures derived using Praat and custom smartphone algorithms across patients with a wide range of vocal pathologies. METHODS: Voice samples were collected from 56 adults (11 vocally healthy, 45 dysphonic, aged 18-80 years) performing three speech tasks: (a) sustained vowel, (b) maximum phonation, and (c) the second and third sentences of the Rainbow passage. Data were analyzed to extract mean fundamental frequency (fo), maximum phonation time (MPT), and cepstral peak prominence (CPP) using Praat and our custom smartphone algorithms. Linear regression models were calculated with and without outliers to determine relationships. RESULTS: Statistically significant relationships were found between the smartphone algorithms and Praat for all three measures (r2 = 0.68-0.95, with outliers; r2 = 0.80-0.98, without outliers). An offset between CPP measures was found where Praat values were consistently lower than those computed by the smartphone app. Outlying data were identified and described, and findings indicated that speakers with high levels of clinician-perceived dysphonia resulted in smartphone algorithm errors. CONCLUSIONS: These results suggest that the proposed algorithms can provide measurements comparable to clinically derived values. However, clinicians should take caution when analyzing severely dysphonic voices as the current algorithms show reduced accuracy for measures of mean fo and MPT for these voice types.