Reliability of Clinical Voice Parameters Captured with Smartphones — Measurements of Added Noise and Spectral Tilt

Schaeffler, Felix; Jannetts, Stephen; Beck, Janet Mackenzie

doi:10.21437/interspeech.2019-2910

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…These results are supported by the GRBAS results of this corpus as well: there is a notably smaller difference of perceived dysphonia between those with and without PD. Although the perceptual evaluation reveals a less distinguishable dysphonia, other factors could be influencing these results, such as the differences in microphones, background noise, and acoustic conditions in the mPower corpus collection method, as suggested in previous studies [64,65].…”

Section: Baseline Resultsmentioning

confidence: 75%

Things to Consider When Automatically Detecting Parkinson’s Disease Using the Phonation of Sustained Vowels: Analysis of Methodological Issues

et al. 2022

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Diagnosing Parkinson’s Disease (PD) necessitates monitoring symptom progression. Unfortunately, diagnostic confirmation often occurs years after disease onset. A more sensitive and objective approach is paramount to the expedient diagnosis and treatment of persons with PD (PwPDs). Recent studies have shown that we can train accurate models to detect signs of PD from audio recordings of confirmed PwPDs. However, disparities exist between studies and may be caused, in part, by differences in employed corpora or methodologies. Our hypothesis is that unaccounted covariates in methodology, experimental design, and data preparation resulted in overly optimistic results in studies of PD automatic detection employing sustained vowels. These issues include record-wise fold creation rather than subject-wise; an imbalance of age between the PwPD and control classes; using too small of a corpus compared to the sizes of feature vectors; performing cross-validation without including development data; and the absence of cross-corpora testing to confirm results. In this paper, we evaluate the influence of these methodological issues in the automatic detection of PD employing sustained vowels. We perform several experiments isolating each issue to measure its influence employing three different corpora. Moreover, we analyze if the perceived dysphonia of the speakers could be causing differences in results between the corpora. Results suggest that each independent methodological issue analyzed has an effect on classification accuracy. Consequently, we recommend a list of methodological steps to be considered in future experiments to avoid overoptimistic or misleading results.

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Section: Baseline Resultsmentioning

confidence: 75%

Things to Consider When Automatically Detecting Parkinson’s Disease Using the Phonation of Sustained Vowels: Analysis of Methodological Issues

et al. 2022

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“…All audio recordings were saved as WAV files. Unlike previous studies, we did not use professional recording hardware in order to maximize the potential telemedical applications of our results 23,24 …”

Section: Methodsmentioning

confidence: 99%

Data mining of an acoustic biomarker in tongue cancers and its clinical validation

et al. 2021

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The promise of speech disorders as biomarkers in clinical examination has been identified in a broad spectrum of neurodegenerative diseases. However, to the best of our knowledge, a validated acoustic marker with established discriminative and evaluative properties has not yet been developed for oral tongue cancers. Here we cross‐sectionally collected a screening dataset that included acoustic parameters extracted from 3 sustained vowels /ɑ/, /i/, /u/ and binary perceptual outcomes from 12 consonant‐vowel syllables. We used a support vector machine with linear kernel function within this dataset to identify the formant centralization ratio (FCR) as a dominant predictor of different perceptual outcomes across gender and syllable. The Acoustic analysis, Perceptual evaluation and Quality of Life assessment (APeQoL) was used to validate the FCR in 33 patients with primary resectable oral tongue cancers. Measurements were taken before (pre‐op) and four to six weeks after (post‐op) surgery. The speech handicap index (SHI), a speech‐specific questionnaire, was also administrated at these time points. Pre‐op correlation analysis within the APeQoL revealed overall consistency and a strong correlation between FCR and SHI scores. FCRs also increased significantly with increasing T classification pre‐operatively, especially for women. Longitudinally, the main effects of T classification, the extent of resection, and their interaction effects with time (pre‐op vs. post‐op) on FCRs were all significant. For pre‐operative FCR, after merging the two datasets, a cut‐off value of 0.970 produced an AUC of 0.861 (95% confidence interval: 0.785–0.938) for T3‐4 patients. In sum, this study determined that FCR is an acoustic marker with the potential to detect disease and related speech function in oral tongue cancers. These are preliminary findings that need to be replicated in longitudinal studies and/or larger cohorts.

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“…Authors concluded that smartphones could be used as a screening tool for voice disorders [ 15 ]. On the other hand, using some other acoustic voice quality parameters could be discouraging [ 16 ]. Two recent studies found that none of the studied smartphones could replace the professional microphone in a voice recording to evaluate the six parameters analyzed, except for f 0 and jitter.…”

Section: Introductionmentioning

confidence: 99%

Reliability of Universal-Platform-Based Voice Screen Application in AVQI Measurements Captured with Different Smartphones

Uloza,

Ulozaitė-Stanienė,

Petrauskas

et al. 2023

JCM

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The aim of the study was to develop a universal-platform-based (UPB) application suitable for different smartphones for estimation of the Acoustic Voice Quality Index (AVQI) and evaluate its reliability in AVQI measurements and normal and pathological voice differentiation. Our study group consisted of 135 adult individuals, including 49 with normal voices and 86 patients with pathological voices. The developed UPB “Voice Screen” application installed on five iOS and Android smartphones was used for AVQI estimation. The AVQI measures calculated from voice recordings obtained from a reference studio microphone were compared with AVQI results obtained using smartphones. The diagnostic accuracy of differentiating normal and pathological voices was evaluated by applying receiver-operating characteristics. One-way ANOVA analysis did not detect statistically significant differences between mean AVQI scores revealed using a studio microphone and different smartphones (F = 0.759; p = 0.58). Almost perfect direct linear correlations (r = 0.991–0.987) were observed between the AVQI results obtained with a studio microphone and different smartphones. An acceptable level of precision of the AVQI in discriminating between normal and pathological voices was yielded, with areas under the curve (AUC) displaying 0.834–0.862. There were no statistically significant differences between the AUCs (p > 0.05) obtained from studio and smartphones’ microphones. The significant difference revealed between the AUCs was only 0.028. The UPB “Voice Screen” application represented an accurate and robust tool for voice quality measurements and normal vs. pathological voice screening purposes, demonstrating the potential to be used by patients and clinicians for voice assessment, employing both iOS and Android smartphones.

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Reliability of Clinical Voice Parameters Captured with Smartphones — Measurements of Added Noise and Spectral Tilt

Cited by 8 publications

References 13 publications

Things to Consider When Automatically Detecting Parkinson’s Disease Using the Phonation of Sustained Vowels: Analysis of Methodological Issues

Things to Consider When Automatically Detecting Parkinson’s Disease Using the Phonation of Sustained Vowels: Analysis of Methodological Issues

Data mining of an acoustic biomarker in tongue cancers and its clinical validation

Reliability of Universal-Platform-Based Voice Screen Application in AVQI Measurements Captured with Different Smartphones

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