Exploring the Feasibility of Using Smartphones for Measuring Sound-Level Difference as a Treatment Outcome.

Area of Science:

• Acoustic analysis
• Speech science
• Biomedical engineering

Background:

• Acoustic voice analysis using smartphones is increasingly popular.
• Clinical environments often have ambient noise, posing challenges for accurate voice measurements.

Purpose of the Study:

• To assess the feasibility of using uncalibrated smartphones as sound-level meters (SLMs) for voice analysis.
• To measure sound-level differences in suboptimal, noisy clinical settings.

Main Methods:

• Four smartphones (2 iPhones, 2 Androids) were compared to a Class 2 SLM.
• Tested linearity and consistency using synthesized tones and sustained /a/ vowels.
• Evaluated performance across varying app choices, distances, sound types, intensities, frequencies, and ambient noise levels (<30 dB, <50 dB).
• Assessed within-device test-retest reliability.

Main Results:

• Strong linear relationships (R² > .980) were observed between smartphones and the Class 2 SLM across all conditions.
• Increased ambient noise (<50 dB) did not impact linearity (R² > .980).
• Near-perfect test-retest reliability (R² > .998, τ > .99) was achieved.
• Good agreement was found in measured differences between white noise tokens (ICC(2,1) = .88).

Conclusions:

• Uncalibrated smartphones are feasible for measuring relative pre-/post-treatment sound levels in sustained vowels.
• Consistent microphone-to-source distance and recording parameters are crucial.
• Absolute sound pressure level measurements require calibration with a gold standard SLM.