Assessing voice health using smartphones: Bias and random error of acoustic voice parameters captured by different smartphone types
Beck, Janet M.
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Jannetts, S., Schaeffler, F., Beck, J. & Cowen, S. (2019) Assessing voice health using smartphones: Bias and random error of acoustic voice parameters captured by different smartphone types. International Journal of Language & Communication Disorders, 54 (2), pp. 292-305.
BACKGROUND: Occupational voice problems constitute a serious public health issue with substantial financial and human consequences for society. Modern mobile technologies like smartphones have the potential to enhance approaches to prevention and management of voice problems. This paper addresses an important aspect of smartphone-assisted voice care: the reliability of smartphone-based acoustic analysis for voice health state monitoring. AIM: To assess the reliability of acoustic parameter extraction for a range of commonly used smartphones by comparison with studio recording equipment. METHODS AND PROCEDURES: Twenty-two vocally healthy speakers (12 female; 10 male) were recorded producing sustained vowels and connected speech under studio conditions using a high-quality studio microphone and an array of smartphones. For both types of utterances, Bland-Altman-Analysis was used to assess overall reliability for Mean F0; CPPS; Jitter (RAP) and Shimmer %. OUTCOMES AND RESULTS: Analysis of the systematic and random error indicated significant bias for CPPS across both sustained vowels and passage reading. Analysis of the random error of the devices indicated that that mean F0 and CPPS showed acceptable random error size, while jitter and shimmer random error was judged as problematic. CONCLUSIONS AND IMPLICATIONS: Confidence in the feasibility of smartphone-based voice assessment is increased by the experimental finding of high levels of reliability for some clinically relevant acoustic parameters, while the use of other parameters is discouraged. We also challenge the practice of using statistical tests (e.g. t-tests) for measurement reliability assessment.