This is the first deep learning ensemble model using COVID-19 emotion analysis to the best of our knowledge.
The AVEDL Model is trained and tested for emotion detection using the COVID-19 labeled tweets and call content of the emergency response support system. Pre-trained transformer-based models BERT, DistilBERT, and RoBERTa are combined to build the AVEDL Model, which achieves the best results. The AVEDL Model is utilized to classify emotion based on COVID-19 associated emergency response support system calls (transcribed) along with tweets. The proposed Average Voting Ensemble Deep Learning model (AVEDL Model) is based on the Average Voting technique. Furthermore, a combined analysis of Twitter patterns connected to emergency services could be valuable in assisting people in this pandemic crisis and understanding and supporting people’s emotions. This study examines the contents of calls landed in the emergency response support system (ERSS) during the pandemic. New psychological services must be established as quickly as possible to support the mental healthcare needs of people in this pandemic condition. People are facing unprecedented levels of intense threat, necessitating professional, systematic psychiatric intervention and assistance. The COVID-19 precautions, lockdown, and quarantine implemented throughout the epidemic resulted in a worldwide economic disaster. In the paper are presented the results of the created application testing and the possibilities of further expansion and improvement of this solution.
#Praat voice software
The paper deals with the user's emotional state classification based on the voice track analysis, it describes its own solution - the measurement and the selection process of appropriate voice characteristics using ANOVA analysis and the use of PRAAT software for many voice aspects analysis and for the implementation of own application to classify the user's emotional state from his/her voice. The goal of adaptive interaction between man and computer is the human needs understanding. One of the interaction possibilities is a voice control which nowadays can‘t be restricted only to direct commands. This development has shown that it is important not only to shift performance and functional boundaries but also to adapt the way human-computer interaction to modern needs. All rights reserved.During the last decades the field of IT has seen an incredible and very rapid development. Application of this task to future studies of dysphonic children may yield clinically valuable information.Ĭhildren Jitter Shimmer Voice diagnostics Voice intensity.Ĭopyright © 2014 Elsevier Ireland Ltd. However, this assessment protocol was suitable only for children above 7 0 years. In phonations at ">80dBA" (10cm distance) voice SPL related effects were considerably reduced. Since the observed confounding effects were large compared to treatment effects, jitter and shimmer may not be meaningful without adequate control of voice SPL. This practical study demonstrated a significant effect of voice loudness and task on jitter and shimmer in children. 19 children below 7 0 years could not perform the voice tasks and were excluded from the study. There were significant differences for jitter and shimmer between all voice tasks (p80dBA". Voice intensity level effects were assessed by descriptive statistics, Analysis of Variance (ANOVA) and Linear Mixed Models (LMM). Jitter (%), shimmer (%) and voice SPL (dBA) were determined using PRAAT. All phonated the vowel/a/for 5s, three times at four defined voice intensity levels (soft/medium/loud/>80dBA) each. Further both their ability to phonate at a prescribed voice intensity level and the effect on SPL related confounding effects were studied.Ī total of 68 healthy children (39 f/29m) aged 5 0 to 9 11 years were included. This cross-sectional single cohort study investigates voice SPL effects on jitter and shimmer in children between 5 0 and 9 11 years phonating at individually "medium" (modeling "comfortable" loudness of the usual clinical protocol), "soft" and "loud" voice and a prescribed intensity level of ">80dBA" (10cm distance, with visual control). However, it is unclear if these findings apply to children and if children are able to control for their own voice intensity. In adults these effects were considerably reduced in phonations with controlled voice SPL >80dBA (10cm distance). In healthy adults and children changes in speaking voice sound pressure level (voice SPL) have significant confounding effects on both parameters. Current voice assessment recommendations for dysphonic children comprise instrumental acoustic measurements of the perturbation parameters jitter and shimmer.
0 kommentar(er)
