Currently submitted to: JMIR Formative Research
Date Submitted: Sep 24, 2025
Open Peer Review Period: Oct 27, 2025 - Dec 22, 2025
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Smartphone‑versus‑Headset Speech‑Feature Pipelines: An Exploratory Study in Healthy Adults
ABSTRACT
Purpose: We aim to create and compare two digital-health pipelines for voice recording and speech analyses based on smartphones and professional recording equipment. We benchmark the quality of the smartphone recordings on speech features previously shown to be predictive of neurological impairment. Approach: A mobile phone app that records a user performing three speech tasks on the smartphone’s built-in microphone is developed. Simultaneously they are recorded using professional recording equipment. Speech features are extracted and compared across each recording pair. Speakers performed three speech tasks – reading a text, repeating syllables for 20 seconds, and sustaining a vowel. Bland-Altman visualization, Deming regression, correlation tests (Spearman, Intraclass) and Kolmogorov-Smirnov tests were utilized to assess concordance degree across devices. Dotplots and standard deviation across speaker and across time were used to assess longitudinal stability. Signal-to-Noise ratios were utilized to assess inter-and-within-rater reproducibility. The robustness of the features over time was assessed by the relative size of differences between speakers and measurement error and profiling the reproducibility of the measurements in a test-retest scenario.
Results:
A corpus of 20 recording sessions was collected from 3 speakers, with one speaker recording 6 sessions spanning 5 months. Twenty-three speech features served as a basis for assessing concordance between recording pairs. 19/23 (80%) features were found to be significantly correlated across recording methods; 10/23 (43%) features have statistically-significant p-values. The smartphone’s noise-cancellation seems to be affecting some features. Most features (14/23, 63%) are longitudinally unstable.
Conclusions:
A voice recording and speech analyses pipeline using a smartphone appears potentially capable of capturing the essential variability of the speech features used for establishing and validating speech biomarkers. Device-dependent noise-reduciton algorithms deployed in modern smartphones need to be handled with care. Much-larger-N Case-Control studies are needed to validate and extend these preliminary findings to clinical setting and across specific neurological disease states.
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