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Mathematical models that accurately simulate the physiological systems of the human body serve as cornerstone instruments for advancing medical science and facilitating innovative clinical interventions. One application is the modeling of the subglottal tract and neck skin properties for its use in the ambulatory assessment of vocal function, by enabling non-invasive monitoring of glottal airflow via a neck surface accelerometer. For the technique to be effective, the development of an accurate building block model for the subglottal tract is required. Such a model is expected to utilize glottal volume velocity as the input parameter and yield neck skin acceleration as the corresponding output. In contrast to preceding efforts that employed frequency-domain methods, the present paper leverages system identification techniques to derive a parsimonious continuous-time model of the subglottal tract using time-domain data samples. Additionally, an examination of the model order is conducted through the application of various information criteria. Once a low-order model is successfully fitted, an inverse filter based on a Kalman smoother is utilized for the estimation of glottal volume velocity and related aerodynamic metrics, thereby constituting the most efficient execution of these estimates thus far. Anticipated reductions in computational time and complexity due to the lower order of the subglottal model hold particular relevance for real-time monitoring. Simultaneously, the methodology proves efficient in generating a spectrum of aerodynamic features essential for ambulatory vocal function assessment. [ABSTRACT FROM AUTHOR]

Emotional communication is a multi-modal phenomenon involving posture, gestures, facial expressions, and the human voice. Affective states systematically modulate the acoustic signals produced during speech production through the laryngeal muscles via the central nervous system, transforming the acoustic signal into a means of affective transmission. Additionally, a substantial body of research in sonobiology has shown that audible acoustic waves (AAW) can affect cellular dynamics. This pilot study explores whether the physical–acoustic changes induced by gratitude states in human speech could influence cell proliferation and Ki67 expression in non-auditory cells (661W cell line). We conduct a series of assays, including affective electroencephalogram (EEG) measurements, an affective text quantification algorithm, and a passive vibro-acoustic treatment (PVT), to control the CO2 incubator environment acoustically, and a proliferation assay with immunolabeling to quantify cell dynamics. Although a larger sample size is needed, the hypothesis that emotions can act as biophysical agents remains a plausible possibility, and feasible physical and biological pathways are discussed. In summary, studying the impact of gratitude AAW on cell biology represents an unexplored research area with the potential to enhance our understanding of the interaction between human cognition and biology through physics principles. [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]

The objective quantification of voice acoustic parameters is used for the diagnosis, monitoring, and treatment of voice disorders. Such assessments are carried out with specialised equipment within the doctor's office. The controlled conditions employed are usually not those of the real environment of the patient. The results, although very informative, are specific to those measurement conditions and to the time when they were performed. A wearable voice monitoring system, based on an accelerometer to ensure the message, can overcome these limitations. We present a miniaturised, low-power, and low-cost wearable system to estimate and record voice fundamental frequency (F0), intensity and phonation time for long intervals in the everyday environment of the patient. It was tested on two subjects for up to two weeks of recording time. It was possible to identify distinct periods in vocal activity, such as normal, professional, demanding or hyperfunctional. It provided information on the workload that the vocal cords needed to cope with over time and when and to what extent that workload was concentrated. The proposed voice dosimetry system enables the extraction and recording of voice parameters for long periods of time in the everyday environment of the patient, allowing the objectification of vocal risk situations and personalised treatment and monitoring. [ABSTRACT FROM AUTHOR]

In the paper, personalized results are presented in the methodology for monitoring information security based on voice authentication. Integration of sound preprocessing and Machine Learning techniques for feature extraction, training, and validation of classification models has been implemented. The objects of research are staked mixed-test voice profiles. Classifies were selected with quantitative evaluation under a threshold of 90.00% by Naive Bayes and Discriminant Analysis. Significantly improved accuracy to approximate levels of 96.0% was established at Decision Tree synthesis. Strongly satisfactory performance indices were reached at the diagnosis of voice profiles using Feed-Forward and Probabilistic Neural Networks, respectively, 98.00% and 100.00%. [ABSTRACT FROM AUTHOR]

The automatic classification of phonation types in singing voice is essential for tasks such as identification of singing style. In this study, it is proposed to use wavelet scattering network (WSN)-based features for classification of phonation types in singing voice. WSN, which has a close similarity with auditory physiological models, generates acoustic features that greatly characterize the information related to pitch, formants, and timbre. Hence, the WSN-based features can effectively capture the discriminative information across phonation types in singing voice. The experimental results show that the proposed WSN-based features improved phonation classification accuracy by at least 9% compared to state-of-the-art features. [ABSTRACT FROM AUTHOR]

Objective: Phonotrauma has been hypothesized to be associated with prolonged and/or accumulated biomechanical stress on vocal fold tissue. This hypothesis can be tested using ambulatory monitoring of vocal fold dissipated power, which requires a reliable method for its noninvasive estimation during the activity of daily living. The first aim of this study was to show that a laboratory-based estimate of vocal fold dissipated power computed from intraoral pressure (IOP) has significant discriminative power in individuals with phonotraumatic vocal hyperfunction (PVH). Considering that estimation of subglottal pressure from IOP is not practical for ambulatory applications, an alternative approach should be used. The second aim of this study was to test the impact of two alternative methods for the estimation of subglottal pressure on the discriminative power of vocal fold dissipated power in individuals with PVH and, hence, to provide an evidence-based recommendation for future ambulatory monitoring studies of vocal fold dissipated power., Method: Four groups of adult females were included in this study: 16 individuals with PVH, 16 individuals with nonphonotraumatic vocal hyperfunction (NPVH), and two groups of vocally typical controls matched to the participants in each patient group in terms of age and occupation. Each participant produced strings of five consecutive /pae/ syllables while wearing a pneumotachograph mask with an IOP tube. Neck-surface accelerometer and acoustic signals were recorded simultaneously using an ambulatory voice monitor and a head-mounted microphone, respectively. IOP was used to estimate subglottal pressure and subject-specific calibration factors were determined for the estimation of subglottal pressure from the accelerometer signal., Results: (1) Individuals with PVH had significantly higher dissipated power than controls (P = 0.001, Cohen's D=1.31) when the intraoral estimate of subglottal pressure was used in the computation of dissipated power. (2) The difference between the dissipated power of individuals with NPVH and their matched controls was not significant. (3) When microphone-based sound pressure levels was used for the estimation of subglottal pressure, the difference between individuals with PVH and their matched controls vanished (P = 0.23). (4) When subject-specific estimation of subglottal pressure from the accelerometer was used, the discriminative power returned with a very large effect size (P = 0.001, D=1.38)., Conclusion: Increased dissipated power is sensitive and specific to individuals with PVH among individuals with hyperfunctional voice disorders. The results provide evidence that accelerometer-based estimate of energy dissipation dose (power integrated over time) during daily life could be clinically useful., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Robert Hillma reports a relationship with InnoVoyce LLC that includes: board membership. Daryush Mehta reports a relationship with InnoVoyce LLC that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)