The Role of Voice Quality in Mandarin Sarcastic Speech: An Acoustic and Electroglottographic Study

2020 ◽  
Vol 63 (8) ◽  
pp. 2578-2588
Author(s):  
Shanpeng Li ◽  
Wentao Gu ◽  
Lei Liu ◽  
Ping Tang
Keyword(s):  
Random Forest ◽  
Gender Difference ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Voice Quality ◽  
Quality Parameters ◽  
Prosodic Features ◽  
Random Forest Algorithm ◽  
Classification Analysis

Purpose Sarcasm is a specialized speech act in daily vocal communication usually characterized by unique prosodic features, but the role of voice quality in expressing sarcasm has not been explored much. The goal of this study is to explore the voice quality features of Mandarin sarcastic speech in comparison to sincere speech. Method Fifteen male and 15 female native speakers of Mandarin uttered 31 target sentences with both sincere and sarcastic attitudes. Nine voice quality parameters extracted from the acoustic and electroglottographic signals were analyzed using a linear mixed model, and a classification analysis using a random forest algorithm was conducted to identify the relative contribution of these parameters to the differentiation between sincere and sarcastic utterances. Results In comparison to sincere speech, sarcastic speech had a creakier voice, which was characterized by a lower fundamental frequency, a greater degree of vocal fold adduction (i.e., higher contact quotient), lesser noise (i.e., higher harmonics-to-noise ratio), and more multiple pulsing (i.e., higher subharmonic-to-harmonic ratio). The interaction effect revealed a gender difference in the use of creakier voice to express sarcasm in Mandarin. The classification analysis using the random forest algorithm showed that the nine voice quality parameters resulted in 84.0% and 83.7% identification rates for sarcastic and sincere utterances, respectively. Conclusions The results of this preliminary study support the role of voice quality in expressing sarcasm in Mandarin speech. Using a set of voice quality parameters, sarcastic and sincere utterances can be effectively identified. Furthermore, there is a gender difference in the use of creakier voice in expressing Mandarin sarcastic speech. Supplemental Material https://doi.org/10.23641/asha.12743780

scholarly journals Distance to supermarkets and dietary variety among Japanese older adults: examining the moderating role of grocery delivery services

2020 ◽  
pp. 1-8
Author(s):  
Kazuhiro Harada ◽  
Kouhei Masumoto ◽  
Shuichi Okada
Keyword(s):  
Older Adults ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Demographic Factors ◽  
Negative Influence ◽  
Dietary Variety ◽  
Delivery Services ◽  
Japanese Older Adults ◽  
Socio Demographic Factors

Abstract Objective: To examine whether using grocery delivery services moderates the relationship between distance to supermarket and dietary variety among Japanese older adults. Design: We conducted a 1-year prospective cohort study. Distance to supermarket was measured using geographic information systems. We collected information on dietary variety score (range 0–10), regular use of grocery delivery services and socio-demographic factors using a questionnaire delivered via post. Setting: The current study was performed in Nada Ward, Kobe City, Japan, from 2017 to 2018. Participants: Older adults living in Nada Ward (n 778). Results: The linear mixed model showed that a longer distance to supermarket (per 100 m: B = –0·07, 95 % CI –0·14, –0·01, P = 0·048) significantly predicted lower dietary variety after adjusting for socio-demographic factors. Using grocery delivery services (B = 0·28, 95 % CI –0·08, 0·64, P = 0·127) did not significantly predict dietary variety, and neither did its interaction with distance to supermarket (B = –0·04, 95 % CI –0·17, 0·10, P = 0·604). Conclusions: The current study found that longer distance to supermarket was associated with lower dietary variety among Japanese older adults and that the use of grocery delivery services did not moderate this association. The findings imply that the use of grocery delivery services is insufficient to reduce the negative influence of inconvenient food access on dietary variety among older adults.

scholarly journals Whole blood levels of S1PR4 mRNA associated with cerebral vasospasm after aneurysmal subarachnoid hemorrhage

2020 ◽  
Vol 133 (6) ◽  
pp. 1837-1841 ◽  
Author(s):  
Anne-Sophie Pulcrano-Nicolas ◽  
Alice Jacquens ◽  
Carole Proust ◽  
Frédéric Clarençon ◽  
Claire Perret ◽  
...  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Cerebral Vasospasm ◽  
Whole Blood ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Experimental Investigations ◽  
Blood Levels ◽  
Sphingosine 1 Phosphate

OBJECTIVEThe authors sought to identify mRNA biomarkers of cerebral vasospasm in whole blood of patients suffering from aneurysmal subarachnoid hemorrhage (aSAH).METHODSA prospective transcriptomic study for vasospasm was conducted in whole blood samples of 44 aSAH patients who developed (VSP+ group, n = 22) or did not develop (VSP− group, n = 22) vasospasm. Samples from all patients were profiled for 21,460 mRNA probes using the Illumina Human HT12v4.0 array. Differential statistical analysis was performed using a linear mixed model.RESULTSLevels of sphingosine-1-phosphate receptor 4 (S1PR4) mRNA were significantly higher (p = 8.03 × 10−6) at presentation in patients who developed vasospasm after aSAH than in patients who did not.CONCLUSIONSThe results, which are consistent with findings of previous experimental investigations conducted in animal models, support the role of S1PR4 and its ligand, sphingosine-1-phosphate (S1P), in arterial-associated vasoconstriction, which suggests that S1PR4 could be used as a biomarker for cerebral vasospasm in aSAH patients.

Familial liability to psychosis is a risk factor for multimorbidity in people with psychotic disorders and their unaffected siblings

2017 ◽  
Vol 45 ◽  
pp. 81-89 ◽  
Author(s):  
M.A. Islam ◽  
M.F.H. Khan ◽  
P.J. Quee ◽  
H. Snieder ◽  
E.R. van den Heuvel ◽  
...  
Keyword(s):  
Risk Factor ◽  
Psychotic Disorders ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Age Groups ◽  
Somatic Complaints ◽  
Independent Effect ◽  
Group Project ◽  
Gender And Age

AbstractBackground:Multimorbidity may impose an overwhelming burden on patients with psychosis and is affected by gender and age. Our aim is to study the independent role of familial liability to psychosis as a risk factor for multimorbidity.Methods:We performed the study within the framework of the Genetic Risk and Outcome of Psychosis (GROUP) project. Overall, we compared 1024 psychotic patients, 994 unaffected siblings and 566 controls on the prevalence of 125 lifetime diseases, and 19 self-reported somatic complaints. Multimorbidity was defined as the presence of two or more complaints/diseases in the same individual. Generalized linear mixed model (GLMM) were used to investigate the effects of gender, age (adolescent, young, older) and familial liability (patients, siblings, controls) and their interactions on multimorbidity.Results:Familial liability had a significant effect on multimorbidity of either complaints or diseases. Patients had a higher prevalence of multimorbidity of complaints compared to siblings (OR 2.20, 95% CI 1.79–2.69, P < 0.001) and to controls (3.05, 2.35–3.96, P < 0.001). In physical health multimorbidity, patients (OR 1.36, 95% CI 1.05–1.75, P = 0.018), but not siblings, had significantly higher prevalence than controls. Similar finding were observed for multimorbidity of lifetime diseases, including psychiatric diseases. Significant results were observed for complaints and disease multimorbidity across gender and age groups.Conclusion:Multimorbidity is a common burden, significantly more prevalent in patients and their unaffected siblings. Familial liability to psychosis showed an independent effect on multimorbidity; gender and age are also important factors determining multimorbidity.

Spain retains its title and sets a new record – generalized linear mixed models on European football championships

2013 ◽  
Vol 9 (1) ◽  
pp. 51-66 ◽  
Author(s):  
Andreas Groll ◽  
Jasmin Abedieh
Keyword(s):  
Variable Selection ◽  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Poisson Model ◽  
Penalty Term ◽  
Sparse Model ◽  
Cluster Data ◽  
European Football

AbstractNowadays many approaches that analyze and predict the results of football matches are based on bookmakers’ ratings. It is commonly accepted that the models used by the bookmakers contain a lot of expertise as the bookmakers’ profits and losses depend on the performance of their models. One objective of this article is to analyze the role of bookmakers’ odds together with many additional, potentially influental covariates with respect to a national team’s success at European football championships and especially to detect covariates, which are able to explain parts of the information covered by the odds. Therefore a pairwise Poisson model for the number of goals scored by national teams competing in European football championship matches is used. Moreover, the generalized linear mixed model (GLMM) approach, which is a widely used tool for modeling cluster data, allows to incorporate team-specific random effects. Two different approaches to the fitting of GLMMs incorporating variable selection are used, subset selection as well as a Lasso-type technique, including an L1-penalty term that enforces variable selection and shrinkage simultaneously. Based on the two preceeding European football championships a sparse model is obtained that is used to predict all matches of the current tournament resulting in a possible course of the European football championship (EURO) 2012.

scholarly journals Does Physical Activity during Alpine Vacations Increase Tourists’ Well-Being?

2019 ◽  
Vol 16 (10) ◽  
pp. 1707 ◽  
Author(s):  
Philipp Schlemmer ◽  
Cornelia Blank ◽  
Martin Schnitzer
Keyword(s):  
Physical Activity ◽  
Everyday Life ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Positive Impact ◽  
Well Being ◽  
Physical Activities ◽  
Time Behavior ◽  
Demographic Patterns

Physical activities have been proven to have an impact on general well-being in everyday life; however, literature lacks an analysis of the effects of physical activities in vacation settings. Thus, the study aimed at assessing the impacts of physical activity on well-being during vacation by taking a longitudinal approach. We utilized a pre-post within-subject design (n = 101) by testing vacationers prior to, during, and after their vacation in an alpine environment. Therefore, a series of eight linear mixed model analyses of co-variance was performed. The results suggested that the duration of a vacation and the amount of physical activity have a positive impact on the components of well-being, which was expressed by changes in the activation, elation, excitement, and calmness subscales of the Mood Survey Scale. Demographic patterns did not reveal any influences. Physical activity might be a marker for well-being, which influences people’s everyday life and leisure time behavior by motivating them to engage in more physical activity. This research extends the existing literature by (1) proving the effects of vacations on well-being, (2) pointing out the effects of demographic predeterminations, and (3) gathering in-depth knowledge about the role of physical activity in changes to well-being.

Resolving homography: The role of post-homograph context in reading aloud ambiguous sentences in Hebrew

2019 ◽  
Vol 40 (6) ◽  
pp. 1405-1420
Author(s):  
Amalia Bar-On ◽  
Elitzur Dattner ◽  
Oriya Braun-Peretz
Keyword(s):  
Ambiguity Resolution ◽  
Mixed Model ◽  
Context Effect ◽  
Linear Mixed Model ◽  
Item Analysis ◽  
The Other ◽  
Read Aloud ◽  
Mixed Model Analysis ◽  
Preceding Context

AbstractThis study examined whether the context immediately succeeding a heterophonic-homographic word (ht-homographic) plays a role in ambiguity resolution during voiced reading of Hebrew. A pretest was designed to find the preferred alternatives of 12 ht-homographic words: 20 adult subjects completed truncated sentences, each ending with a homographic word, preceded by a context allowing for both of its alternatives to be read. Following the pretest, each word was embedded in four research conditions determined by post-homographic context (keeping preceding context constant): two adjacent revealing contexts, one supporting the preferred alternative and the other the un-preferred alternative; and two distant revealing contexts, one supporting the preferred alternative and the other the un-preferred alternative. Four lists of 12 sentences, each including the four conditions, were then read aloud by four groups of 20 adults. Results from a generalized linear mixed-model analysis showed that the immediately succeeding context affected the deciphering of un-preferred alternatives in voiced reading. An item analysis further showed that highly preferred alternatives were less prone to the immediately succeeding context effect than slightly preferred alternatives. We conclude that the context immediately succeeding a ht-homographic word plays a role in ambiguity resolution during voiced reading, through interactions with the word’s lexical and syntactic characteristics.

Double Jeopardy Versus Double Benefit: The Role of Age and Leisure Activities in the Incidence of Disability Among Chinese Older Adults

2020 ◽  
pp. 089826432098366
Author(s):  
Rongjun Sun
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Leisure Activities ◽  
Time Lag ◽  
Double Jeopardy ◽  
Chinese Older Adults ◽  
Healthy Longevity ◽  
Disability Status ◽  
Over Time

Objectives: This paper analyzes the double jeopardy effect of age and double benefit of leisure activities in the incidence of disability. Methods: This study uses data from the Chinese Longitudinal Healthy Longevity Survey between 2002 and 2014. Disability status is measured by activities of daily living. Leisure activities include physical and social activities. A generalized linear mixed model with a time-lag design is used to analyze the trajectory of being disabled. Results: Older ages are associated with double jeopardy of disability: higher initial probability and faster pace. The double benefit of leisure activities is confirmed: lower initial probability and a slower pace of change in disability over time. The age pattern is substantially alleviated when leisure activities and other covariates are present. Discussion: Although the risk of disability rises with advancing age, the over-time trajectory can be flattened by engagement in leisure activities and other factors.

scholarly journals Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

2020 ◽  
Author(s):  
Gregory L Watson ◽  
Di Xiong ◽  
Lu Zhang ◽  
Joseph A Zoller ◽  
John Shamshoian ◽  
...  
Keyword(s):  
New York ◽  
Random Forest ◽  
Compartmental Model ◽  
Mixed Model ◽  
Population Level ◽  
Velocity Model ◽  
Random Forest Algorithm ◽  
Nonlinear Mixed Model ◽  
Uncertainty Estimates ◽  
The U.S

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian nonlinear mixed model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectory. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for infections and deaths in U.S. states. We evaluate forecasting accuracy on a two-week holdout set, finding that the model predicts COVID-19 cases and deaths well, with a mean absolute scaled error of 0.40 for cases and 0.32 for deaths throughout the two-week evaluation period. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Ohio, and Mississippi. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

Understanding the Relationship between Mood Symptoms and Mobile App Engagement Among Breast Cancer Patients: A Machine Learning Process (Preprint)

2021 ◽  
Author(s):  
Anna N Baglione ◽  
Lihua Cai ◽  
Aram Bahrini ◽  
Isabella Posey ◽  
Mehdi Boukhechba ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mental Health ◽  
Random Forest ◽  
Cancer Patients ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Self Report ◽  
Breast Cancer Patients ◽  
The Relationship

BACKGROUND Health interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success, yet the relationship between mood and engagement among cancer patients remains poorly understood. One reason is the lack of a data-driven process for analyzing mood and app engagement data for cancer patients. OBJECTIVE The purpose of this study is to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in breast cancer patients. We describe the steps of data preprocessing, feature extraction, and data modeling and prediction. We then apply this process as a case study to data collected from breast cancer patients who engaged with a mobile mental health app intervention (IntelliCare) over 7-weeks. We compare engagement patterns over time (e.g., frequency, days of use) between high- and low-anxious and high- and low-depressed participants. We then use a Linear Mixed Model to identify significant effects and evaluate the performance of Random Forest and XGBoost classifiers in predicting weekly state mood from baseline affect and engagement features. METHODS We describe the steps of data preprocessing, feature extraction, and data modeling and prediction. We then apply this process as a case study to data collected from breast cancer patients who engaged with a mobile mental health app intervention (IntelliCare) over 7-weeks. We compare engagement patterns over time (e.g., frequency, days of use) between high- and low-anxious and high- and low-depressed participants. We then use a Linear Mixed Model to identify significant effects and evaluate the performance of Random Forest and XGBoost classifiers in predicting weekly state mood from baseline affect and engagement features. RESULTS We observed differences in engagement patterns between high- and low-anxious and depressed participants. Linear Mixed Model results varied by the featureset; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. Accuracy of predicting state mood varied according to classifier and featureset. The XGBoost classifier achieved the highest accuracy for state anxiety prediction when self-report scores and engagement features were used for only the most highly-used apps. The Random Forest classifier achieved the highest accuracy for state depression prediction when self-report scores and engagement features were used from all apps. CONCLUSIONS The results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in breast cancer patients. The ability to leverage both self-report and engagement features to predict state mood during an intervention could be used to enhance decision-making for researchers and clinicians, as well as assist in developing more personalized interventions for breast cancer patients.

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