Smartphones Offer New Opportunities in Clinical Voice Research

Manfredi, Claudia; Lebacq, Jean; Cantarella, Giovanna; Schoentgen, Jean; Orlandi, Silvia; Bandini, Andrea; Dejonckere, Philippe H.

doi:10.1016/j.jvoice.2015.12.020

Cited by 65 publications

(46 citation statements)

References 16 publications

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“…() used the same synthetic voice as in Manfredi et al . () and added external noise to the recordings. They found that for jitter measures and Noise to Harmonincs Ratio (NHR), good reliability remains up to ambient noise levels of 50 dB A .…”

Section: Discussionmentioning

confidence: 99%

“…, Manfredi et al . ). Both studies interpreted the plots visually but did not use the bias and limits of agreement in their assessment of agreement.…”

Section: Background and Motivationmentioning

confidence: 97%

“…Of the studies so far comparing smartphone devices with studio microphones, only two have included Bland-Altman plots (Uloza et al 2015, Manfredi et al 2016. Both studies interpreted the plots visually but did not use the bias and limits of agreement in their assessment of agreement.…”

Section: Acoustic Voice Monitoring For Voice Health Interventionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing voice health using smartphones: bias and random error of acoustic voice parameters captured by different smartphone types

Jannetts

Schaeffler

Beck

et al. 2019

Intl J Lang & Comm Disor

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Background Occupational voice problems constitute a serious public health issue with substantial financial and human consequences for society. Modern mobile technologies such as 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 & 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 utterance, Bland–Altman analysis was used to assess overall reliability for mean F0, cepstral peak prominence (CPPS), Jitter (RAP) and Shimmer %. Outcomes & 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 & 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.

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Section: Discussionmentioning

confidence: 99%

“…, Manfredi et al . ). Both studies interpreted the plots visually but did not use the bias and limits of agreement in their assessment of agreement.…”

Section: Background and Motivationmentioning

confidence: 97%

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Assessing voice health using smartphones: bias and random error of acoustic voice parameters captured by different smartphone types

Jannetts

Schaeffler

Beck

et al. 2019

Intl J Lang & Comm Disor

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“…1 In a first experiment, we demonstrated the reliability of smartphones with regard to quality of recordings over a wide range of degrees of deviance (perturbation and additive noise) and in the male and female ranges of fundamental frequency (F0) values. The comparison was carried out using realistic synthesized voice signals (sustained /a:/ altered by three levels of jitter and three levels of noise, the two basic acoustic voice quality parameters), which guarantee exact knowledge of reference values for voice quality parameters.…”

Section: Introductionmentioning

confidence: 99%

“…However, practical limits of tolerable ambient noise intensity values for the application considered here-that is, a patient recording his or her voice at home or at the workplace for sending to the voice clinic-are not accurately known. Furthermore, in our first paper, 1 only sustained /a:/ was used as voice material. It is thus worthwhile to assess the extent to which smartphones possibly distort synthesized samples comparable with natural voice productions, for which different reference values for F0 perturbation and noise to harmonics (N:H) ratio are exactly known.…”

Section: Introductionmentioning

confidence: 99%

Maximal Ambient Noise Levels and Type of Voice Material Required for Valid Use of Smartphones in Clinical Voice Research

Lebacq¹,

Schoentgen

Cantarella

et al. 2017

Journal of Voice

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Summary: Purpose. Smartphone technology provides new opportunities for recording standardized voice samples of patients and transmitting the audio files to the voice laboratory. This drastically improves the achievement of baseline designs, used in research on efficiency of voice treatments. However, the basic requirement is the suitability of smartphones for recording and digitizing pathologic voices (mainly characterized by period perturbations and noise) without significant distortion. In a previous article, this was tested using realistic synthesized deviant voice samples (/a:/) with three precisely known levels of jitter and of noise in all combinations. High correlations were found between jitter and noise to harmonics ratio measured in (1) recordings via smartphones, (2) direct microphone recordings, and (3) sound files generated by the synthesizer. In the present work, similar experiments were performed (1) in the presence of increasing levels of ambient noise and (2) using synthetic deviant voice samples (/a:/) as well as synthetic voice material simulating a deviant short voiced utterance (/aiuaiuaiu/). Results. Ambient noise levels up to 50 dBA are acceptable. However, signal processing occurs in some smartphones, and this significantly affects estimates of jitter and noise to harmonics ratio when formant changes are introduced in analogy with running speech. The conclusion is that voice material must provisionally be limited to a sustained /a/.

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Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

Evangelista,

Kale,

McCutcheon

et al. 2023

The Laryngoscope

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IntroductionAccuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice data are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research.ObjectiveThe aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research.MethodsA 30‐question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research.ResultsSeventy‐two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%–60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi‐institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%).ConclusionTo conduct large‐scale multi‐institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing.Level of EvidenceLevel 5 Laryngoscope, 2023

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Smartphones Offer New Opportunities in Clinical Voice Research

Cited by 65 publications

References 16 publications

Assessing voice health using smartphones: bias and random error of acoustic voice parameters captured by different smartphone types

Assessing voice health using smartphones: bias and random error of acoustic voice parameters captured by different smartphone types

Maximal Ambient Noise Levels and Type of Voice Material Required for Valid Use of Smartphones in Clinical Voice Research

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

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