Improved Estimation of Parkinsonian Vowel Quality through Acoustic Feature Assimilation.

This paper investigated the performance of a number of acoustic measures, both individually and in combination, in predicting the perceived quality of sustained vowels produced by people impaired with Parkinson's disease (PD). Sustained vowel recordings were collected from 51 PD patients before and after the administration of the Levodopa medication. Subjective ratings of the overall vowel quality were garnered using a visual analog scale. These ratings served to benchmark the effectiveness of the acoustic measures. Acoustic predictors of the perceived vowel quality included the harmonics-to-noise ratio (HNR), smoothed cepstral peak prominence (CPP), recurrence period density entropy (RPDE), Gammatone frequency cepstral coefficients (GFCCs), linear prediction (LP) coefficients and their variants, and modulation spectrogram features. Linear regression (LR) and support vector regression (SVR) models were employed to assimilate multiple features. Different feature dimensionality reduction methods were investigated to avoid model overfitting and enhance the prediction capabilities for the test dataset. Results showed that the RPDE measure performed the best among all individual features, while a regression model incorporating a subset of features produced the best overall correlation of 0.80 between the predicted and actual vowel quality ratings. This model may therefore serve as a surrogate for auditory-perceptual assessment of Parkinsonian vowel quality. Furthermore, the model may offer the clinician a tool to predict who may benefit from Levodopa medication in terms of enhanced voice quality.

Obese communities among the best predictors of COVID-19-related deaths.

Coronavirus disease 2019 (COVID-19) is the largest outbreak to strike the world since the Spanish flu in 1918. Visual examination of the world map shows a wide variation of death tolls between countries. The main goal of our series is to determine the best predictors of such discrepancy. METHODS: This is a retrospective study in which the rate of COVID-19 deaths was correlated with each of the following independent variables: total tests per 1 million population, gross domestic product (GDP), average temperatures per country, ultraviolet index, median age, average BMI per country, food supply, Bacille Calmette-Guerin compulsory status, and passenger traffic. RESULTS: BMI per country proved to be the second best predictor of death rate with an R value of 0.43, and GDP being the best predictor with R = 0.65. CONCLUSION: This article shows a tight correlation between average BMI, food supply per country, and COVID-19-related deaths. Such predisposing factors might operate by upregulating the inflammation pathway in heavily struck countries, leading to easier triggering of the infamous cytokine storm syndrome. Obesity also increases cardiovascular and respiratory morbidities, which are coupled to increased ICU demand and deaths among infected cases.Video abstract: http://links.lww.com/CAEN/A25.

Objective and Subjective Speech Quality Assessment of Amplification Devices for Patients With Parkinson's Disease.

This paper investigated subjective and objective assessment of Parkinsonian speech quality. Speech stimuli were recorded from 11 Parkinsonian and 10 age-matched normal control participants under different amplification and environmental conditions. Quality ratings of the recorded stimuli were obtained from naïve listeners. For objective assessment, feature vectors were derived from the speech recordings based on temporal, spectral, and/or cepstral parametrization. These feature vectors were subsequently mapped to the predicted quality scores through several regression methods, including support vector regression, Gaussian process regression, and deep learning. Analyses of subjective speech quality ratings showed that Parkinsonian speech quality was significantly poorer than control subjects' speech quality, and that the amplification devices differentially affected perceived quality of Parkinsonian speech. Objective analyses revealed disparity in performance among feature vectors and mappers, with some feature vector and mapper combinations exhibiting statistically similar correlations with subjective ratings. A set consisting of cepstral, spectral, and modulation domain speech features when combined with Gaussian process regression or deep learning resulted in the highest correlation of 0.85 with the subjective data.

Objective and Subjective Assessment of Amplified Parkinsonian Speech Quality.

Hypophonia is a common speech impairment associated with Parkinson's disease (PD). Voice amplifiers are typically used to increase voice loudness, but little is known about their impact on perceived speech quality. In this paper, speech recordings were obtained from 11 PD subjects with and without the use of seven different amplification devices, and in the absence or presence of background noise. The recorded speech samples were rated for their sound quality by 10 naive listeners. The same speech recordings were analyzed objectively, where in linear prediction, mel-frequency cepstral coefficients (MFCCs), and gammatone cepstral coefficients (GFCCs) were extracted and mapped to predicted quality scores using linear regression and Support Vector Regression (SVR). Results showed that amplification devices differentially affect the perceived quality of PD speech, that objective and subjective quality scores correlated well, and that a reduced set of GFCC features mapped with SVR produced the best correlation with the subjective scores.