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Purpose: The Daily Phonotrauma Index (DPI) can quantify pathophysiological mechanisms associated with daily voice use in individuals with phonotraumatic vocal hyperfunction (PVH). Since DPI was developed based on weeklong ambulatory voice monitoring, this study investigated if DPI can achieve comparable performance using (a) short laboratory speech tasks and (b) fewer than 7 days of ambulatory data. Method: An ambulatory voice monitoring system recorded the vocal function/behavior of 134 females with PVH and vocally healthy matched controls in two different conditions. In the laboratory, the participants read the first paragraph of the Rainbow Passage and produced spontaneous speech (in-lab data). They were then monitored for 7 days (in-field data). Separate DPI models were trained from the in-lab and in-field data using the standard deviation of the difference between the magnitude of the first two harmonics (H1–H2) and the skewness of neck-surface acceleration magnitude. First, 10-fold cross-validation evaluated the classification performance of the in-lab and in-field DPIs. Second, the effect of the number of ambulatory monitoring days on the accuracy of in-field DPI classification was quantified. Results: The average in-lab DPI accuracy computed from the Rainbow Passage and spontaneous speech were 57.9% and 48.9%, respectively, which are close to chance performance. The average classification accuracy of the in-field DPI was significantly higher with a very large effect size (73.4%, Cohen’s d = 1.8). Next, the average in-field DPI accuracy increased from 66.5% for 1 day to 75.0% for 7 days, with the gain of including an additional day on accuracy dropping below 1 percentage point after 4 days. Conclusions: The DPI requires ambulatory monitoring data as its discriminative power diminished significantly once computed from short in-lab recordings. Additionally, ambulatory monitoring should sample multiple days to achieve robust performance. The result of this research note can be used to make an informed decision about the trade-off between classification accuracy and cost of data collection. [ABSTRACT FROM AUTHOR]

Purpose: Although different factors and voice measures have been associated with phonotraumatic vocal hyperfunction (PVH), it is unclear what percentage of individuals with PVH exhibit such differences during their daily lives. This study used a machine learning approach to quantify the consistency with which PVH manifests according to ambulatory voice measures. Analyses included acoustic parameters of phonation as well as temporal aspects of phonation and rest, with the goal of determining optimally consistent signatures of PVH. Method: Ambulatory neck-surface acceleration signals were recorded over 1 week from 116 female participants diagnosed with PVH and age-, sex-, and occupation-matched vocally healthy controls. The consistency of the manifestation of PVH was defined as the percentage of participants in each group that exhibited an atypical signature based on a target voice measure. Evaluation of each machine learning model used nested 10-fold cross-validation to improve the generalizability of findings. In Experiment 1, we trained separate logistic regression models based on the distributional characteristics of 14 voice measures and durations of voicing and resting segments. In Experiments 2 and 3, features of voicing and resting duration augmented the existing distributional characteristics to examine whether more consistent signatures would result. Results: Experiment 1 showed that the difference in the magnitude of the first two harmonics (H1–H2) exhibited the most consistent signature (69.4% of participants with PVH and 20.4% of controls had an atypical H1–H2 signature), followed by spectral tilt over eight harmonics (73.6% participants with PVH and 32.1% of controls had an atypical spectral tilt signature) and estimated sound pressure level (SPL; 66.9% participants with PVH and 27.6% of controls had an atypical SPL signature). Additionally, 77.6% of participants with PVH had atypical resting duration, with 68.9% exhibiting atypical voicing duration. Experiments 2 and 3 showed that augmenting the best-performing voice measures with univariate features of voicing or resting durations yielded only incremental improvement in the classifier’s performance. Conclusions: Females with PVH were more likely to use more abrupt vocal fold closure (lower H1–H2), phonate louder (higher SPL), and take shorter vocal rests. They were also less likely to use higher fundamental frequency during their daily activities. The difference in the voicing duration signature between participants with PVH and controls had a large effect size, providing strong empirical evidence regarding the role of voice use in the development of PVH. [ABSTRACT FROM AUTHOR]

Purpose: Many studies using machine learning (ML) in speech, language, and hearing sciences rely upon cross-validations with single data splitting. This study's first purpose is to provide quantitative evidence that would incentivize researchers to instead use the more robust data splitting method of nested k-fold cross-validation. The second purpose is to present methods and MATLAB code to perform power analysis for ML-based analysis during the design of a study. Method: First, the significant impact of different cross-validations on ML outcomes was demonstrated using real-world clinical data. Then, Monte Carlo simulations were used to quantify the interactions among the employed crossvalidation method, the discriminative power of features, the dimensionality of the feature space, the dimensionality of the model, and the sample size. Four different cross-validation methods (single holdout, 10-fold, train--validation--test, and nested 10-fold) were compared based on the statistical power and confidence of the resulting ML models. Distributions of the null and alternative hypotheses were used to determine the minimum required sample size for obtaining a statistically significant outcome (5% significance) with 80% power. Statistical confidence of the model was defined as the probability of correct features being selected for inclusion in the final model. Results: ML models generated based on the single holdout method had very low statistical power and confidence, leading to overestimation of classification accuracy. Conversely, the nested 10-fold cross-validation method resulted in the highest statistical confidence and power while also providing an unbiased estimate of accuracy. The required sample size using the single holdout method could be 50% higher than what would be needed if nested k-fold crossvalidation were used. Statistical confidence in the model based on nested k-fold cross-validation was as much as four times higher than the confidence obtained with the single holdout--based model. A computational model, MATLAB code, and lookup tables are provided to assist researchers with estimating the minimum sample size needed during study design. Conclusion: The adoption of nested k-fold cross-validation is critical for unbiased and robust ML studies in the speech, language, and hearing sciences. Supplemental Material: https://doi.org/10.23641/asha.25237045 [ABSTRACT FROM AUTHOR]