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.

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.

Magnitude of Neck-Surface Vibration as an Estimate of Subglottal Pressure During Modulations of Vocal Effort and Intensity in Healthy Speakers.

Purpose: This study examined the relationship between the magnitude of neck-surface vibration (NSVMag; transduced with an accelerometer) and intraoral estimates of subglottal pressure (P'sg) during variations in vocal effort at 3 intensity levels. Method: Twelve vocally healthy adults produced strings of /pɑ/ syllables in 3 vocal intensity conditions, while increasing vocal effort during each condition. Measures were made of P'sg (estimated during stop-consonant closure), NSVMag (measured during the following vowel), sound pressure level, and respiratory kinematics. Mixed linear regression was used to analyze the relationship between NSVMag and P'sg with respect to total lung volume excursion, levels of lung volume initiation and termination, airflow, laryngeal resistance, and vocal efficiency across intensity conditions. Results: NSVMag was significantly related to P'sg (p < .001), and there was a significant, although small, interaction between NSVMag and intensity condition. Total lung excursion was the only additional variable contributing to predicting the NSVMag-P'sg relationship. Conclusions: NSVMag closely reflects P'sg during variations of vocal effort; however, the relationship changes across different intensities in some individuals. Future research should explore additional NSV-based measures (e.g., glottal airflow features) to improve estimation accuracy during voice production.

Real-time estimation of aerodynamic features for ambulatory voice biofeedback.

The development of ambulatory voice monitoring devices has the potential to improve the diagnosis and treatment of voice disorders. In this proof-of-concept study, real-time biofeedback is incorporated into a smartphone-based platform that records and processes neck surface acceleration. The focus is on utilizing aerodynamic measures of vocal function as a basis for biofeedback. This is done using regressed Z-scores to compare recorded values to normative estimates based on sound pressure level and fundamental frequency. Initial results from the analysis of different voice qualities suggest that accelerometer-based estimates of aerodynamic parameters can be used for real-time ambulatory biofeedback.