Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comparative Study Across Three Datasets.

OBJECTIVE: This study evaluates the efficacy of voice analysis combined with machine learning (ML) techniques in enabling the diagnosis of Parkinson's disease (PD). METHODS: Voice data, phonation of the vowel "a," from three distinct datasets (two from the University of California Irvine ML Repository and one from figshare) for 432 participants (278 PD patients) were analyzed. We employed four ML models-Artificial Neural Networks, Random Forest, Gradient Boosting (GB), and Support Vector Machine (SVM)-alongside two ensemble methods (soft voting classifier-Ensemble Voting Classifier and stacking method-Ensemble Stacking Model (ESM)). The models underwent 50 iterations of evaluation, involving various data splits and 10-fold cross-validation. Comparative analysis was done using one-way Analysis of Variance followed by Bonferroni posthoc corrections. RESULTS: The ESM, SVM, and GB models emerged as the top performers, demonstrating superior performance across metrics, including accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver operating characteristic curve (ROC AUC). Despite data heterogeneity and variable selection limitations, the models showed high values for all metrics. CONCLUSIONS: ML integration with voice analysis, mainly through ESM, SVM, and GB, is promising for early PD diagnosis. Using multi-source data and a large sample size enhances our findings' validity, reliability, and generalizability. SIGNIFICANCE: Integrating advanced ML techniques with voice analysis demonstrates substantial potential for improving early PD detection, offering valuable tools for speech-language pathologists (SLPs). These findings provide clinically relevant insights that can be applied within the scope of SLP practice to refine diagnostic processes and facilitate early intervention.

Prediction of dysphagia aspiration through machine learning-based analysis of patients' postprandial voices.

BACKGROUND: Conventional diagnostic methods for dysphagia have limitations such as long wait times, radiation risks, and restricted evaluation. Therefore, voice-based diagnostic and monitoring technologies are required to overcome these limitations. Based on our hypothesis regarding the impact of weakened muscle strength and the presence of aspiration on vocal characteristics, this single-center, prospective study aimed to develop a machine-learning algorithm for predicting dysphagia status (normal, and aspiration) by analyzing postprandial voice limiting intake to 3 cc. METHODS: Conducted from September 2021 to February 2023 at Seoul National University Bundang Hospital, this single center, prospective cohort study included 198 participants aged 40 or older, with 128 without suspected dysphagia and 70 with dysphagia-aspiration. Voice data from participants were collected and used to develop dysphagia prediction models using the Multi-Layer Perceptron (MLP) with MobileNet V3. Male-only, female-only, and combined models were constructed using 10-fold cross-validation. Through the inference process, we established a model capable of probabilistically categorizing a new patient's voice as either normal or indicating the possibility of aspiration. RESULTS: The pre-trained models (mn40_as and mn30_as) exhibited superior performance compared to the non-pre-trained models (mn4.0 and mn3.0). Overall, the best-performing model, mn30_as, which is a pre-trained model, demonstrated an average AUC across 10 folds as follows: combined model 0.8361 (95% CI 0.7667-0.9056; max 0.9541), male model 0.8010 (95% CI 0.6589-0.9432; max 1.000), and female model 0.7572 (95% CI 0.6578-0.8567; max 0.9779). However, for the female model, a slightly higher result was observed with the mn4.0, which scored 0.7679 (95% CI 0.6426-0.8931; max 0.9722). Additionally, the other models (pre-trained; mn40_as, non-pre-trained; mn4.0 and mn3.0) also achieved performance above 0.7 in most cases, and the highest fold-level performance for most models was approximately around 0.9. The 'mn' in model names refers to MobileNet and the following number indicates the 'width_mult' parameter. CONCLUSIONS: In this study, we used mel-spectrogram analysis and a MobileNetV3 model for predicting dysphagia aspiration. Our research highlights voice analysis potential in dysphagia screening, diagnosis, and monitoring, aiming for non-invasive safer, and more effective interventions. TRIAL REGISTRATION: This study was approved by the IRB (No. B-2109-707-303) and registered on clinicaltrials.gov (ID: NCT05149976).

Reflux Symptom Index (RSI), Videolaryngostroboscopy and Voice Analysis: A Triad of Non-Invasive Tools to Study Treatment Outcomes of Laryngopharyngeal Reflux Disease (LPRD).

Study post treatment improvement of Laryngopharyngeal Reflux Disease (LPRD) using non-invasive tools of Reflux symptom index (RSI), Reflux finding score (RFS) grading of videolaryngostroboscopy (VLS) and voice analysis. This study from December 2020 to April 2022 enrolled 100 adults with complaints suggestive of reflux symptoms and having Reflux Symptom Index (RSI) more than 13. All patients underwent VLS along with voice analysis. VLS findings were graded using Reflux Finding Score (RFS). Patients were advised for lifestyle modifications and proton pump inhibitors for 8 weeks when post treatment RSI, VLS and voice analyses were again documented. The age range was from 18 to 75 years. Males predominated. Lifestyle modification compliance was seen in 85% of the patients. We found a significant association (P = 0.001) for difference in pretreatment and posttreatment for both Reflux Symptom Index (RSI) parameters & Reflux Finding Score Index (RFS) parameters. Voice analysis pre and post treatment showed a significant association (P = 0.001) for fundamental frequency, jitter, shimmer, harmonic-to-noise ratio and maximum phonation time. The gold standard of diagnosis of LPRD is 24 h pH monitoring but has many false negatives and false positives due to intermittent reflux and inaccurate probe placement. This costly, time consuming and invasive procedure is not widely available amongst our speciality. Excellent visualisation of VLS allowed accurate RFS calculation. Voice analysis permitted early diagnosis of LPRD induced hoarseness before it became clinically significant. It also documented the treatment outcome. We conclude that an 8-weeks proton pump inhibitor treatment combined with lifestyle modification resulted in a significant improvement in the parameters of the non-invasive tools of RSI and RFS and voice analysis.

Critical swallowing functions contributing to dysphagia in patients with recurrent laryngeal nerve paralysis after esophagectomy.

BACKGROUND: Recurrent laryngeal nerve paralysis (RLNP) after esophagectomy can cause aspiration because of incomplete glottis closure, leading to pneumonia. However, patients with RLNP often have preserved swallowing function. This study investigated factors that determine swallowing function in patients with RLNP. METHODS: Patients with esophageal cancer who underwent esophagectomy and cervical esophagogastric anastomosis were enrolled between 2017 and 2020. Videofluoroscopic examination of swallowing study (VFSS) and acoustic voice analysis were performed on patients with suspected dysphagia including RLNP. Dysphagia in VFSS was defined as score ≥ 3 of the 8-point penetration-aspiration scale VFSS and acoustic analysis results related to dysphagia were compared between patients with and without RLNP. RESULTS: Among 312 patients who underwent esophagectomy, 74 developed RLNP. The incidence of late-onset pneumonia was significantly higher in the RLNP group than in the non-RLNP (18.9 vs. 8.0%, P = .008). Detailed swallowing function was assessed by VFSS in 84 patients, and patients with RLNP and dysphagia showed significantly shorter maximum diagonal hyoid bone elevation (10.62 vs. 16.75 mm; P = .003), which was a specific finding not seen in patients without RLNP. For acoustic voice analysis, the degree of hoarseness was not closely related to dysphagia. The length of oral intake rehabilitation for patients with and without RLNP was comparable if they did not present with dysphagia (8.5 vs. 9.0 days). CONCLUSIONS: Impaired hyoid bone elevation is a specific dysphagia factor in patients with RLNP, suggesting compensatory epiglottis inversion by hyoid bone elevation is important for incomplete glottis closure caused by RLNP.

Voice acoustic characteristics of children with late-onset cochlear implantation: Correlation to auditory performance.

OBJECTIVES: To study the voice acoustic parameters of congenitally deaf children with delayed access to sounds due to late-onset cochlear implantation and to correlate their voice characteristics with their auditory performance. METHODS: The study included 84 children: a control group consisting of 50 children with normal hearing and normal speech development; and a study group consisting of 34 paediatric cochlear implant (CI) recipients who had suffered profound hearing loss since birth. According to speech recognition scores and pure-tone thresholds, the study group was further subdivided into two subgroups: 24 children with excellent auditory performance and 10 children with fair auditory performance. The mean age at the time of implantation was 3.6 years for excellent auditory performance group and 3.2 years for fair auditory performance group. Voice acoustic analysis was conducted on all study participants. RESULTS: Analysis of voice acoustic parameters revealed a statistically significant delay in both study groups in comparison to the control group. However, there was no statistically significant difference between the two study groups. DISCUSSION: Interestingly, in both excellent and fair performance study groups, the gap in comparison to normal hearing children was still present. While late-implanted children performed better on segmental perception (e.g. word recognition), suprasegmental perception (e.g. as demonstrated by objective acoustic voice analysis) did not progress to the same extent. CONCLUSION: On the suprasegmental speech performance level, objective acoustic voice measurements demonstrated a significant delay in the suprasegmental speech performance of children with late-onset CI, even those with excellent auditory performance.

The impact of tamoxifen treatment on voice parameters in premenopausal women with breast cancer.

OBJECTIVE: The study aims to investigate the estrogen-agonistic effects of tamoxifen on voice parameters in premenopausal women diagnosed with breast cancer. METHODS: A total of 108 premenopausal women were included, segmented into distinct treatment groups and a control group. Objective sound analysis was conducted using robust statistical methods, employing SPSS 25.0 for data analysis. RESULTS: The study identified a statistically significant reduction in Jitter values across all treatment groups compared to the control group. No significant changes were observed in other voice quality parameters such as F0, Shimmer, NHR, and HNR. CONCLUSIONS: The findings suggest that tamoxifen may have an estrogen-agonistic effect on voice quality, thereby potentially influencing future treatment protocols. This research fills a critical void in existing literature and sets the stage for more comprehensive studies that consider affects of hormonal therapies to voice.

High and Wide: An In Silico Investigation of Frequency, Intensity, and Vibrato Effects on Widely Applied Acoustic Voice Perturbation and Noise Measures.

OBJECTIVES: This in silico study explored the effects of a wide range of fundamental frequency (fo), source-spectrum tilt (SST), and vibrato extent (VE) on commonly used frequency and amplitude perturbation and noise measures. METHOD: Using 53 synthesized tones produced in Madde, the effects of stepwise increases in fo, intensity (modeled by decreasing SST), and VE on the PRAAT parameters jitter % (local), relative average perturbation (RAP) %, shimmer % (local), amplitude perturbation quotient 3 (APQ3) %, and harmonics-to-noise ratio (HNR) dB were investigated. A secondary experiment was conducted to determine whether any fo effects on jitter, RAP, shimmer, APQ3, and HNR were stable. A total of 10 sinewaves were synthesized in Sopran from 100 to 1000 Hz using formant frequencies for /a/, /i/, and /u/-like vowels, respectively. All effects were statistically assessed with Kendall's tau-b and partial correlation. RESULTS: Increasing fo resulted in an overall increase in jitter, RAP, shimmer, and APQ3 values, respectively (P < 0.01). Oscillations of the data across the explored fo range were observed in all measurement outputs. In the Sopran tests, the oscillatory pattern seen in the Madde fo condition remained and showed differences between vowel conditions. Increasing intensity (decreasing SST) led to reduced pitch and amplitude perturbation and HNR (P < 0.05). Increasing VE led to lower HNR and an almost linear increase of all other measures (P < 0.05). CONCLUSION: These novel data offer a controlled demonstration for the behavior of jitter (local) %, RAP %, shimmer (local) %, APQ3 %, and HNR (dB) when varying fo, SST, and VE in synthesized tones. Since humans will vary in all of these aspects in spoken language and vowel phonation, researchers should take potential resonance-harmonics type effects into account when comparing intersubject or preintervention and postintervention data using these measures.

PVGAN: A Pathological Voice Generation Model Incorporating a Progressive Nesting Strategy.

The voice generation task is to solve the problem of limited samples in the voice dataset using computer technology. By increasing the number of samples, the accuracy of voice disorder diagnosis can be improved, which has a wide range of application value in medical diagnosis and other fields. At present, there are insufficient models for detailed features such as pitch, timbre, and different frequency components in pathological voice data. Therefore, this paper proposes a PVGAN network for learning different frequency information of audio to generate pathological voice data. The proposed network captures the multi-scale features and different periodic patterns of audio signals by designing multiscale perceptual residual blocks and periodic discriminators. At the same time, a progressive nesting strategy was proposed to combine the generator and the discriminator to improve the learning ability of different resolution information. In addition, a latent mapping network is designed to fuse the latent vector with the condition information to generate sound features related to specific diseases or pathological states. The loss function is optimized to further improve the model performance. On the Saarbruecken Voice Database(SVD), the average values of each index of the data generated after training with different pathological types as conditional information are similar to the original data. Finally, the generated data were used to expand the SVD dataset, and the accuracy of the two classification experiments was improved to a certain extent.

Effects of deep brain stimulation of the subthalamic nucleus on patients with Parkinson's disease: a machine-learning voice analysis.

Introduction: Deep brain stimulation of the subthalamic nucleus (STN-DBS) can exert relevant effects on the voice of patients with Parkinson's disease (PD). In this study, we used artificial intelligence to objectively analyze the voices of PD patients with STN-DBS. Materials and methods: In a cross-sectional study, we enrolled 108 controls and 101 patients with PD. The cohort of PD was divided into two groups: the first group included 50 patients with STN-DBS, and the second group included 51 patients receiving the best medical treatment. The voices were clinically evaluated using the Unified Parkinson's Disease Rating Scale part-III subitem for voice (UPDRS-III-v). We recorded and then analyzed voices using specific machine-learning algorithms. The likelihood ratio (LR) was also calculated as an objective measure for clinical-instrumental correlations. Results: Clinically, voice impairment was greater in STN-DBS patients than in those who received oral treatment. Using machine learning, we objectively and accurately distinguished between the voices of STN-DBS patients and those under oral treatments. We also found significant clinical-instrumental correlations since the greater the LRs, the higher the UPDRS-III-v scores. Discussion: STN-DBS deteriorates speech in patients with PD, as objectively demonstrated by machine-learning voice analysis.

Utility of Smart Phones as a Voice Acquisition Device for Assessing Pre and Post Treatment Voice Using PRAAT.

Voice assessment before and after treatment helps the clinician to assess the effectiveness of the treatment given and facilitates comparison between different treatment modalities. Voice handicap index -10(VHI-10) questionnaire is a tool which allows the voice to be evaluated subjectively from the patient's perspective. PRAAT is a freely available, software programme that acoustically analyse voice signals. Smart phones are widely used and the high quality of the embedded microphone in it makes it a suitable and easily available voice recording device. This study aims at using PRAAT and VHI-10 questionnaire in evaluating voice before and after treatment. The utility of smart phones as a voice acquisition device is also explored in the study. Prospective, observational study, carried out from 1st November 2019 to 30th September 2021in the ENT out- patient department at a tertiary hospital in Punjab. 58 patients complaining of dysphonia were enrolled consecutively in the study. All patients underwent detailed history, examination of the larynx using 70-degree rigid laryngoscope. The voice handicap was scored by (VHI-10) questionnaire and acoustic evaluation of voice was done using the PRAAT software. Patients' voice was further evaluated 3 months post-therapy with VHI 10 questionnaire and acoustic analysis. The parameters measured on PRAAT were mean pitch, jitter (local), shimmer (local), and mean harmonics to noise ratio (HNR). The voice was recorded using a smart phone and later transferred onto a laptop for analysis. The pre and post treatment acoustic parameters and VHI-10 scores were compared and correlated. There was significant difference (p < 0.001) between the pre and post treatment VHI-10 scores and all the acoustic parameters measured except for median pitch (p = 0.995). A poor positive correlation was found between the pre treatment VHI-10 scores and jitter(r = 0.188, p = 0.157) and shimmer (r = 0.288, p = 0.028) values. A negative correlation was observed between pre treatment VHI-10 scores and pitch (r = - 0.151, p = 0.259) and HNR(r = - 0.424, p = 0.001). Post treatment VHI-10 scores showed positive correlation with jitter (r = 0.302, p = 0.021) and shimmer (0.162, p = 0.225) values and negative correlation with pitch (r = - 0.10, p = 0.457) and HNR (r = - 0.356, p = 0.006) values. We found significant differences in the VHI-10 scores and PRAAT voice analysis results before and after treatment in patients complaining with voice change (dysphonia). VHI-10 questionnaire and PRAAT are good and convenient tools for assessing the voice subjectively and objectively. Only a poor to fair correlation was found between VHI-10 scores and PRAAT analysis results. More studies must be done to confirm the utility of smart phones as a voice acquisition device and PRAAT software in voice analysis.

Dysphonia in Children; Clinical Profile, Conservative Treatment Modalities and Outcomes: An Institutional Experience.

Dysphonia in children represents broad-spectrum voice problems. Global prevalence of hoarseness in school-aged children is 6-23%. It impairs communication of child, thus affects social life. This study shows importance of appropriate preventive measures for paediatric dysphonia and diagnosis of voice problems in early stages. Continuous voice abuse in children can cause recurrent voice disorders as well as speech problems. This prospective study included 104 patients(78 males and 26 females) of 6-15 years, with voice problems, in a tertiary care centre, North Kerala, during June 2022-March 2023. Chief complaints and risk factors evaluated. Voice analysis using maximum phonation time (MPT), Child Voice Handicap Index for Parents (CVHI-10-P), Reflux Symptom Index (RSI) and clinical examination including indirect laryngoscopy (IDL) and 70degree scopy were done. Treatment given for a maximum of 2 weeks. Advised voice rest and voice therapy throughout. All patients followed up after 2 weeks and up to 3 months. Voice abuse was the commonest risk factor and voice change, the commonest symptom. MPT reduced in 23% males and 14% females. According to CVHI-10-P, screaming was present in 52% children and symptoms present mostly in afternoon. RSI identified the role of LPRD in dysphonia. IDL and 70 scopy identified most common diagnosis as vocal nodule. Treatment given and follow-up period noted. All except vocal polyp had complete relief. Most common diagnosis was vocal nodule which resulted from chronic voice abuse. Appropriate preventive measures, early diagnosis and adequate treatment of voice problems should be considered. Conservative management in early stages is recommended.

Voice analysis results in individuals with Alzheimer's disease: How do age and cognitive status affect voice parameters?

BACKGROUND: Reports of acoustic changes in the voice in individuals with Alzheimer's disease (AD) and the relationship of acoustic changes with age and cognitive status are still limited. OBJECTIVE: This study aims to determine the changes in voice analysis results in AD, as well as the effects of age and cognitive status on voice parameters. METHODS: The study included 47 (AD: 30; healthy: 17) women with a mean age of 76.13 years. The acoustic voice parameters mean fundamental frequency (F0), relative average perturbation (RAP), jitter percent (Jitt), shimmer percent (Shim), and noise-to-harmonic ratio were detected. The mini-mental state examination (MMSE) was utilized. RESULTS: F0, Shim, Jitt, and RAP values were found to be statistically significantly higher in individuals with AD compared to healthy individuals. There was a significant negative correlation between MMSE and F0, Jitt, RAP and Shim, and the MMSE score had a significant negative effect on F0, Jitt, and RAP (p < .05). CONCLUSION: Cognitive status was discovered to significantly impact the voice, with fundamental frequency and frequency and amplitude perturbations increasing as cognitive level decreases. In order to contribute to the therapy process for voice disorders, cognitive functions can be focused on in addition to voice therapy.

On the inter-dataset generalization of machine learning approaches to Parkinson's disease detection from voice.

BACKGROUND AND OBJECTIVE: Parkinson's disease is the second-most-common neurodegenerative disorder that affects motor skills, cognitive processes, mood, and everyday tasks such as speaking and walking. The voices of people with Parkinson's disease may become weak, breathy, or hoarse and may sound emotionless, with slurred words and mumbling. Algorithms for computerized voice analysis have been proposed and have shown highly accurate results. However, these algorithms were developed on single, limited datasets, with participants possessing similar demographics. Such models are prone to overfitting and are unsuitable for generalization, which is essential in real-world applications. METHODS: We evaluated the computerized Parkinson's disease diagnosis performance of various machine learning models and showed that these models degraded rapidly when used on different datasets. We evaluated two mainstream state-of-the-art approaches, one based on deep convolutional neural networks and another based on voice feature extraction followed by a shallow classifier (i.e., extreme gradient boosting (XGBoost)). RESULTS: An investigation with four datasets (CzechPD, PC-GITA, ITA, and RMIT-PD) proved that even if the algorithms yielded excellent performance on a single dataset, the results obtained on new data or even a mix of datasets were very unsatisfactory. CONCLUSIONS: More work needs to be done to make computerized voice analysis methods for Parkinson's disease diagnosis suitable for real-world applications.

Acoustic Voice Analysis as a Useful Tool to Discriminate Different ALS Phenotypes.

Approximately 80-96% of people with amyotrophic lateral sclerosis (ALS) become unable to speak during the disease progression. Assessing upper and lower motor neuron impairment in bulbar regions of ALS patients remains challenging, particularly in distinguishing spastic and flaccid dysarthria. This study aimed to evaluate acoustic voice parameters as useful biomarkers to discriminate ALS clinical phenotypes. Triangular vowel space area (tVSA), alternating motion rates (AMRs), and sequential motion rates (SMRs) were analyzed in 36 ALS patients and 20 sex/age-matched healthy controls (HCs). tVSA, AMR, and SMR values significantly differed between ALS and HCs, and between ALS with prevalent upper (pUMN) and lower motor neuron (pLMN) impairment. tVSA showed higher accuracy in discriminating pUMN from pLMN patients. AMR and SMR were significantly lower in patients with bulbar onset than those with spinal onset, both with and without bulbar symptoms. Furthermore, these values were also lower in patients with spinal onset associated with bulbar symptoms than in those with spinal onset alone. Additionally, AMR and SMR values correlated with the degree of dysphagia. Acoustic voice analysis may be considered a useful prognostic tool to differentiate spastic and flaccid dysarthria and to assess the degree of bulbar involvement in ALS.

Reinke's Edema: New Insights into Voice Analysis, a Retrospective Study.

PURPOSE: Reinke's edema (RE) is a pathological condition involving increased volume of the vocal folds and resulting in significant impact on speech, fundamental frequency, and vocal range. Literature reports few studies which analyze vocal features according to the severity of RE. The aims of this study were to investigate the aerodynamics, acoustic characteristics, and sound spectrograms of a group of RE patients and to assess whether there was any correlation with their endoscopic grading. METHODS: A total of 98 patients were included in the study, 49 patients with RE and 49 healthy volunteers (HV). Multidimensional Voice Program was used to perform objective voice assessment. Maximum phonation time (MPT) and Voice Handicap Index (VHI) questionnaire were collected. The spectrograms of the vowel /a/ and of the word /aiuole/, which contains the five Italian vowels, of each patient were analyzed according to the classification of Yanaghiara modified by Ricci Maccarini and De Colle. Laryngological assessment was used to record vocal folds morphology according to Yonekawa's classification. Univariate analysis was used to compare group outcomes. Bivariate analysis was used to compare endoscopic grading and voice analysis results. RESULTS: Univariate analysis of the HV and RE groups revealed statistically significant differences (P < 0.05) for the following parameters: jitter%, shimmer%, harmonic-to-noise ratio (NHR), voice turbulence index (VTI), MPT, VHI except for soft phonation index. Spearman's rank correlation showed a positive correlation between vocal parameters such as jitter%, shimmer%, NHR, VTI, and RE gradings. A negative correlation was found between MPT and RE gradings. Bivariate analysis indicated a strong positive correlation between RE grading and the spectrogram classification performed both with the vowel / a / (Rho 0.86; P = 0.0001) and with the word / aiuole / (Rho 0.81; P = 0.0001). CONCLUSION: The present study demonstrates that patients with RE have different voice characteristics compared to HV. In particular, the voice analysis highlighted acoustic parameters that correlated to differing degrees of RE. In addition, spectrogram analysis should be considered for acoustic assessments before and after medical and surgical therapy and also in forensic medicine.

Introduction of Voice Type Component (VTC) as an Effective Acoustic Voice Analysis Method in Tele-evaluation.

OBJECTIVE: Telepractice in voice health care and evaluation services has attracted much attention in recent years. Multiple studies have proven the effectiveness of voice therapy with telepractice. However, voice evaluations are still mostly conducted in person due to the lack of sensitive acoustic analysis methods. METHODS: This study examined various acoustic analysis methods for voice evaluation in telepractice. Eighteen female elementary school teachers with self-reported voice disorders volunteered to participate in the study. Speech samples were collected before and after the interventions using two voice sampling methods concurrently. One set of data was collected using the traditional voice sample collection method by the therapist in person. The second set of data was collected on the same speech samples using the clients' own smartphones, and the collected voice samples were later sent to the researcher for further acoustic analysis. The voice type component (VTC) measurement represented the proportion of different VTCs in a voice by measuring the chaos and intrinsic dimension. RESULTS: Voice analyses were conducted on both sets of data, and the correlation between the two sampling procedures was analyzed. It appears that the VTC could be a more reliable method for producing acoustic analysis results with voice samples collected from smartphones compared to other objective voice assessment procedures. This reliability has been demonstrated via statistical analysis, including correlation coefficient, pairwise t test, d-prime, and area under the curve. The results of this study highlighted the VTC as an effective and accurate acoustic analysis method in tele-evaluation. CONCLUSIONS: This feasible voice sampling method, which utilizes participants' own smartphones, will reduce barriers to accessing limited voice specialists due to distance and will decrease the cost of care by minimizing expenses associated with travel and additional equipment for voice sampling. Ultimately, this approach will enhance the effectiveness of voice care delivered through telepractice to patients in remote and underserved areas.

It Sounds like It Feels: Preliminary Exploration of an Aeroacoustic Diagnostic Protocol for Singers.

To date, no established protocol exists for measuring functional voice changes in singers with subclinical singing-voice complaints. Hence, these may go undiagnosed until they progress into greater severity. This exploratory study sought to (1) determine which scale items in the self-perceptual Evaluation of Ability to Sing Easily (EASE) are associated with instrumental voice measures, and (2) construct as proof-of-concept an instrumental index related to singers' perceptions of their vocal function and health status. Eighteen classical singers were acoustically recorded in a controlled environment singing an /a/ vowel using soft phonation. Aerodynamic data were collected during a softly sung /papapapapapapa/ task with the KayPENTAX Phonatory Aerodynamic System. Using multi and univariate linear regression techniques, CPPS, vibrato jitter, vibrato shimmer, and an efficiency ratio (SPL/PSub) were included in a significant model (p < 0.001) explaining 62.4% of variance in participants' composite scores of three scale items related to vocal fatigue. The instrumental index showed a significant association (p = 0.001) with the EASE vocal fatigue subscale overall. Findings illustrate that an aeroacoustic instrumental index may be useful for monitoring functional changes in the singing voice as part of a multidimensional diagnostic approach to preventative and rehabilitative voice healthcare for professional singing-voice users.

The Comparison of Electroacoustic and Subjective Voice Quality in Type 3 and 4 Transoral Laser Cordectomy for Early-Stage Glottic Carcinoma.

One of the main issues in early-stage glottic carcinoma management is the voice quality following different types of treatment modalities. In type 3 and 4 transoral laser cordectomies, the voice outcomes can show significant differences due to the extent of the vocal muscle resection. This study aims to compare the voice quality in patients who underwent type 3 and 4 laser cordectomy for early-stage glottic carcinoma. A total of 30 patients who underwent type 3 (15 patients) and type 4 (15 patients) laser cordectomy for T1a glottic carcinoma between May 2018 and 2020 were included in this retrospective comparative study. Electroacoustic voice analysis and Voice Handicap Index-10 were performed in the postoperative twelfth month and the outcomes were compared between two laser cordectomy groups. The mean age of all patients was 48.6 ± 4.2 years. Noise-to-harmonic ratio, jitter, shimmer, pitch perturbation quotient and amplitude perturbation quotient values were significantly different between two groups (p < 0.05). Fundamental frequency and Voice Handicap Index-10 scores showed no statistically significant difference (p > 0.05). This study reports significantly better results for type 3 against type 4 laser cordectomy by means of objective voice analysis outcomes, except fundamental frequency. On the other hand, self-reported subjective analysis showed very similar results for both groups. Further studies combining data with multiple objective and subjective analyses with larger patient series and longer follow-up are warranted.

A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents.

Sleep-deprived fatigued person is likely to commit more errors that may even prove to be fatal. Thus, it is necessary to recognize this fatigue. The novelty of the proposed research work for the detection of this fatigue is that it is nonintrusive and based on multimodal feature fusion. In the proposed methodology, fatigue is detected by obtaining features from four domains: visual images, thermal images, keystroke dynamics, and voice features. In the proposed methodology, the samples of a volunteer (subject) are obtained from all four domains for feature extraction, and empirical weights are assigned to the four different domains. Young, healthy volunteers (n = 60) between the age group of 20 to 30 years participated in the experimental study. Further, they abstained from the consumption of alcohol, caffeine, or other drugs impacting their sleep pattern during the study. Through this multimodal technique, appropriate weights are given to the features obtained from the four domains. The results are compared with k-nearest neighbors (kNN), support vector machines (SVM), random tree, random forest, and multilayer perceptron classifiers. The proposed nonintrusive technique has obtained an average detection accuracy of 93.33% in 3-fold cross-validation.