Acoustic comparison and speech-based biomarkers for the detection of gastroesophageal reflux disease: Research-grade versus iPad recordings

Abstract

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.