Evidence-Based Recommendations for Tablet Recordings From the Bridge2AI-Voice Acoustic Experiments

Abstract

Background: As part of a larger goal to create best practices for voice data collection to fuel voice artificial intelligence (AI) research, the objective of this study was to investigate the ability of readily available iOS and Android tablets with and without low-cost headset microphones to produce recordings and subsequent acoustic measures of voice comparable to "research quality" instrumentation.

Methods: Recordings of 24 sustained vowel samples representing a wide range of typical and disordered voices were played via a head-and-torso model and recorded using a research quality standard microphone/preamplifier/audio interface. Acoustic measurements from the standard were compared with two popular tablets using their built-in microphones and with low-cost headset microphones at different distances from the mouth.

Results: Voice measurements obtained via tablets + headset microphones close to the mouth (2.5 and 5 cm) strongly correlated (r's > 0.90) with the research standard and resulted in no significant differences for measures of vocal frequency and perturbation. In contrast, voice measurements obtained using the tablets' built-in microphones at typical reading distances (30 and 45 cm) tended to show substantial variability in measurement, greater mean differences in voice measurements, and relatively poorer correlations vs the standard.

Conclusion: Findings from this study support preliminary recommendations from the Bridge2AI-Voice Consortium recommending the use of smartphones paired with low-cost headset microphones as adequate methods of recording for large-scale voice data collection from a variety of clinical and nonclinical settings. Compared with recording using a tablet direct, a headset microphone controls for recording distance and reduces the effects of background noise, resulting in decreased variability in recording quality.

Data availability: Data supporting the results reported in this article may be obtained upon request from the contact author.

Keywords: AI research—Voice—Acoustic analysis—Perturbation—Cepstral analysis.