scholarly journals The Investigation the Effects of the Performance of an Independent Emotion Recognition of Model Used in the Dimensioning of Emotions

2016 ◽  
Vol 2 (3) ◽  
pp. 26
Author(s):  
Turgut Özseven ◽  
Muharrem Düğenci
Keyword(s):  
Emotion Recognition ◽  
Acoustic Analysis ◽  
Objective Evaluation ◽  
Dimensional Structure ◽  
Support Vector ◽  
Dimensional Model ◽  
Acoustic Parameters ◽  
Categorical Structure ◽  
Recognition Success ◽  
The Impact

Emotion recognition aims at determining the state of emotion which is included in the speech or mimics of a person. Emotion recognition from speech is an area related to signal processing and psychology. Acoustic parameters obtained from speech signals through acoustic analysis, which is one of objective evaluation methods, is intensively used in emotion recognition studies. In this paper success in emotion recognition is examined in categorical aspects and the impact of dimensional model on independent emotion recognition success is investigated. Acoustic parameters were subjected to classification with Support Vector Machine in order to determine emotion recognition success. According to the obtained findings, emotion recognition success in categorical structure, dimensional structure and categorical-dimensional structure were 69.5%, 73.3% and 87.1%, respectively. Even if dimensional structure is higher in arousal than in valence, when emotion recognition success is examined on each emotion dimension, valence provided higher success.

scholarly journals The Investigation the Effects of the Performance of an Independent Emotion Recognition of Model Used in the Dimensioning of Emotions

2016 ◽  
Vol 2 (3) ◽  
pp. 26
Author(s):  
Turgut Özseven ◽  
Muharrem Düğenci
Keyword(s):  
Emotion Recognition ◽  
Acoustic Analysis ◽  
Objective Evaluation ◽  
Dimensional Structure ◽  
Support Vector ◽  
Dimensional Model ◽  
Acoustic Parameters ◽  
Categorical Structure ◽  
Recognition Success ◽  
The Impact

Emotion recognition aims at determining the state of emotion which is included in the speech or mimics of a person. Emotion recognition from speech is an area related to signal processing and psychology. Acoustic parameters obtained from speech signals through acoustic analysis, which is one of objective evaluation methods, is intensively used in emotion recognition studies. In this paper success in emotion recognition is examined in categorical aspects and the impact of dimensional model on independent emotion recognition success is investigated. Acoustic parameters were subjected to classification with Support Vector Machine in order to determine emotion recognition success. According to the obtained findings, emotion recognition success in categorical structure, dimensional structure and categorical-dimensional structure were 69.5%, 73.3% and 87.1%, respectively. Even if dimensional structure is higher in arousal than in valence, when emotion recognition success is examined on each emotion dimension, valence provided higher success.

scholarly journals The Investigation the Effects of the Performance of an Independent Emotion Recognition of Model Used in the Dimensioning of Emotions

2016 ◽  
Vol 2 (3) ◽  
pp. 26
Author(s):  
Turgut Özseven ◽  
Muharrem Düğenci
Keyword(s):  
Emotion Recognition ◽  
Acoustic Analysis ◽  
Objective Evaluation ◽  
Dimensional Structure ◽  
Support Vector ◽  
Dimensional Model ◽  
Acoustic Parameters ◽  
Categorical Structure ◽  
Recognition Success ◽  
The Impact

Emotion recognition aims at determining the state of emotion which is included in the speech or mimics of a person. Emotion recognition from speech is an area related to signal processing and psychology. Acoustic parameters obtained from speech signals through acoustic analysis, which is one of objective evaluation methods, is intensively used in emotion recognition studies. In this paper success in emotion recognition is examined in categorical aspects and the impact of dimensional model on independent emotion recognition success is investigated. Acoustic parameters were subjected to classification with Support Vector Machine in order to determine emotion recognition success. According to the obtained findings, emotion recognition success in categorical structure, dimensional structure and categorical-dimensional structure were 69.5%, 73.3% and 87.1%, respectively. Even if dimensional structure is higher in arousal than in valence, when emotion recognition success is examined on each emotion dimension, valence provided higher success.

scholarly journals The Investigation the Effects of the Performance of an Independent Emotion Recognition of Model Used in the Dimensioning of Emotions

2016 ◽  
Vol 5 (1) ◽  
pp. 26
Author(s):  
Turgut Özseven ◽  
Muharrem Düğenci
Keyword(s):  
Emotion Recognition ◽  
Acoustic Analysis ◽  
Objective Evaluation ◽  
Dimensional Structure ◽  
Support Vector ◽  
Dimensional Model ◽  
Acoustic Parameters ◽  
Categorical Structure ◽  
Recognition Success ◽  
The Impact

Emotion recognition aims at determining the state of emotion which is included in the speech or mimics of a person. Emotion recognition from speech is an area related to signal processing and psychology. Acoustic parameters obtained from speech signals through acoustic analysis, which is one of objective evaluation methods, is intensively used in emotion recognition studies. In this paper success in emotion recognition is examined in categorical aspects and the impact of dimensional model on independent emotion recognition success is investigated. Acoustic parameters were subjected to classification with Support Vector Machine in order to determine emotion recognition success. According to the obtained findings, emotion recognition success in categorical structure, dimensional structure and categorical-dimensional structure were 69.5%, 73.3% and 87.1%, respectively. Even if dimensional structure is higher in arousal than in valence, when emotion recognition success is examined on each emotion dimension, valence provided higher success.

scholarly journals Acoustic Analysis Before and After Voice Therapy for Laryngeal Pathology

2017 ◽  
Vol 13 (4) ◽  
pp. 323-327 ◽  
Author(s):  
S.S. Chhetri ◽  
R. Gautam
Keyword(s):  
Acoustic Analysis ◽  
Alternative Therapy ◽  
Vocal Folds ◽  
Objective Evaluation ◽  
Voice Therapy ◽  
Good Effect ◽  
Acoustic Parameters ◽  
Significant Difference ◽  
Before And After ◽  
The Voice

Background Voice problems caused by pathologies in vocal folds are well known. Some types of laryngeal pathologies have certain acoustic characteristics. Objective evaluation helps characterize the voice and voice problems providing supporting evidences, severity of disorders. It helps assess the response to the treatment and measures the outcomes.Objective The objective of the study is to determine the effectiveness of the voice therapy and quantify the results objectively by voice parameters.Method Study includes 61 patients who presented with different types of laryngeal pathologies. Acoustic analyses and voice assessment was done with Dr. Speech ver 4 (Tiger DRS Inc.). Acoustic parameters including fundamental frequency, jitters, shimmers, Harmonic to noise ratio (HNR), Normalized noise energy (NNE) were analyzed before and after voice therapy.Result Bilateral vocal nodules were the most common pathologies comprising 44.26%. All acoustic parameters showed a significant difference after the therapy (p<0.05) except for NNE. Dysphonia due to vocal fold polyp showed no improvement even after voice therapy (p>0.05).Conclusion Acoustic analysis provides an objective, recordable data regarding the voice parameters and its pathologies. Though, few pathology require alternative therapy rather than voice therapy, overall it has a good effect on glottic closure. As the voice therapy can improve the different indices of voice, it can be viewed as imperative part of treatment and to monitor progression.

Speech task affects the objective evaluation of dysphonic voices

2018 ◽  
Vol 7 (1) ◽  
pp. 1-15
Author(s):  
Wellington da Silva ◽  
Ana Carolina Constantini
Keyword(s):  
Repeated Measures ◽  
Acoustic Analysis ◽  
Objective Assessment ◽  
Voice Quality ◽  
Objective Evaluation ◽  
Connected Speech ◽  
Acoustic Parameters ◽  
Speech Task ◽  
Acoustic Evaluation ◽  
Cepstral Peak Prominence

The acoustic analysis of speech has proved useful in the clinical evaluation of dysphonia, for it allows an objective assessment of the voice. However, the literature has suggested that the type of speech task used to obtain voice samples from patients (sustained vowel or connected speech) may affect both the perceptual and the acoustic evaluation of dysphonic voices. This study aimed at investigating whether the type of speech task significantly influences the acoustic analysis of dysphonic voices. Five acoustic parameters related to voice quality (cepstral peak prominence, difference between the magnitudes of the first and second harmonics, harmonics-to-noise ratio, jitter and shimmer) were automatically computed from voice samples of 5 female and 5 male subjects with and without dysphonia. These recordings consisted of three types of speech task: connected speech, count and sustained vowel. Analyses of variance with repeated measures showed that all five acoustic parameters were significantly affected by speech task. Further analyses through the Duncan’s multiple-range test indicated that the type of speech task may also influence the discrimination of dysphonic voices. It is concluded that speech task affects the acoustic assessment of dysphonic voices by significantly raising or reducing the values of the acoustic parameters.

Emotion recognition and confidence ratings predicted by vocal stimulus type and acoustic parameters

2019 ◽  
Author(s):  
Adi Lausen ◽  
Kurt Hammerschmidt
Keyword(s):  
Emotion Recognition ◽  
Stimulus Type ◽  
Emotional Expressions ◽  
Acoustic Features ◽  
Acoustic Parameters ◽  
Performance Accuracy ◽  
The Impact ◽  
The Voice ◽  
Vocal Emotion

Human speech expresses emotional meaning not only through semantics, but also through certain attributes of the voice, such as pitch or loudness. In investigations of vocal emotion recognition, there is considerable variability in the types of stimuli and procedures used to examine their influence on emotion recognition. In addition, a person’s confidence in the assessments of another person’s emotional state has been argued to strongly influence performance accuracy in emotion recognition tasks. Nevertheless, such associations have rarely been studied previously. We addressed this gap by examining the impact of vocal stimulus type and prosodic speech attributes on emotion recognition and a person’s confidence in a given response. We analysed a total of 1038 emotional expressions according to a baseline set of 13 prosodic acoustic parameters. Results showed that these parameters provided sufficient discrimination between expressions of emotional categories to permit accurate statistical classification. Emotion recognition and confidence judgments were found to depend on stimulus material as they could be predicted by different constellations of acoustic features. Finally, results indicated that the correct classification of emotional expressions elicited increased confident judgments. Together, these findings show that vocal stimulus type and prosodic attributes of speech strongly influence emotion recognition and listeners’ confidence in these given responses.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

scholarly journals The Impact of Urban Recreation Environment on Residents’ Happiness—Based on a Case Study in China

2021 ◽  
Vol 13 (10) ◽  
pp. 5549
Author(s):  
Lei Kang ◽  
Zhaoping Yang ◽  
Fang Han
Keyword(s):  
Low Income ◽  
Subjective Evaluation ◽  
Objective Evaluation ◽  
Income Group ◽  
High Income Group ◽  
Rapid Urbanization ◽  
Indirect Impact ◽  
Negative Effect ◽  
The Impact ◽  
Urban Recreation

Rapid urbanization promotes the expansion of urban tourism and recreation functions, but it also brings many problems, which affect residents’ happiness. Previous studies have emphasized the direct impact of urban recreation environment on happiness, and few have explored the indirect impact of urban recreation environment on happiness through subjective evaluation. Based on the survey data of nearly 10,000 permanent residents in 40 key tourism cities in China, this paper establishes a theoretical framework of the direct and indirect impact of urban recreation environment on happiness. The objective evaluation of natural recreation environment and sociocultural recreation environment has an important influence on happiness, but the influence of natural recreation environment is greater than that of sociocultural recreation environment. Individual subjective satisfaction with urban recreation environment mediates the relationship between urban objective environment and happiness. Urban parks have a positive effect on happiness, while tourist attractions have a negative effect. The influence of urban location on happiness is nonlinear. The high-income group is more sensitive to the recreation environment, while the low-income group is less sensitive to the recreation environment. These findings provide insights for further improving citizens’ quality of life and designing urban construction in developing countries under the conditions of rapid urbanization.

scholarly journals Forest and Society’s Welfare: Impact Assessment in Lithuania

2021 ◽  
Vol 13 (10) ◽  
pp. 5598
Author(s):  
Stasys Mizaras ◽  
Diana Lukmine
Keyword(s):  
Impact Assessment ◽  
Forest Policy ◽  
Assessment Model ◽  
Dimensional Model ◽  
Important Goal ◽  
Welfare Impact ◽  
Global Problem ◽  
Five Dimensional Model ◽  
Welfare Index ◽  
The Impact

Effective formation and implementation of forest policy can only be achieved with orientation to the most important goal—increasing society’s welfare. The global problem is, at present, that the impact of forests on society welfare indexes have not been identified. The aim of the study is to design an assessment model and assess the impact of Lithuanian forests on the society welfare index. The impact of forests was determined by multiplying the country’s welfare of society index by the forest contribution coefficient. In this study, to assess the index of the welfare of Lithuanian society, a five-dimensional model with 16 indicators was applied. The study is based on the Eurostat database and on Lithuanian forestry statistics. The Lithuanian welfare of society index calculated according to the model was 51.4% and the contribution of forests in this index was 3.9%. It represented 7.6% of the index of the welfare of society. Forests have the greatest impact in the environmental dimension, according to the assessment results.

scholarly journals Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain

2021 ◽  
Vol 11 (2) ◽  
pp. 796
Author(s):  
Alhanoof Althnian ◽  
Duaa AlSaeed ◽  
Heyam Al-Baity ◽  
Amani Samha ◽  
Alanoud Bin Dris ◽  
...  
Keyword(s):  
Empirical Evaluation ◽  
Classification Performance ◽  
Support Vector ◽  
Robust Model ◽  
Original Distribution ◽  
C4.5 Decision Tree ◽  
Dataset Size ◽  
Overall Performance ◽  
Medical Domain ◽  
The Impact

Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.

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