Two-Level Classification in Determining the Age and Gender Group of a Speaker

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Ergün Yücesoy
Keyword(s):  
Classification Accuracy ◽  
Age Groups ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Optimum Number ◽  
Mel Frequency Cepstral Coefficients ◽  
Age And Gender ◽  
Number Of Components ◽  
And Gender

In this study, the classification of the speakers according to age and gender was discussed. Age and gender classes were first examined separately, and then by combining these classes a classification with a total of 7 classes was made. Speech signals represented by Mel-Frequency Cepstral Coefficients (MFCC) and delta parameters were converted into Gaussian Mixture Model (GMM) mean supervectors and classified with a Support Vector Machine (SVM). While the GMM mean supervectors were formed according to the Maximum-a-posteriori (MAP) adaptive GMM-Universal Background Model (UBM) configuration, the number of components was changed from 16 to 512, and the optimum number of components was decided. Gender classification accuracy of the system developed using aGender dataset was measured as 99.02% for two classes and 92.58% for three classes and age group classification accuracy was measured as 67.03% for female and 63.79% for male. In the classification of age and gender classes together in one step, an accuracy of 61.46% was obtained. In the study, a two-level approach was proposed for classifying age and gender classes together. According to this approach, the speakers were first divided into three classes as child, male and female, then males and females were classified according to their age groups and thus a 7-class classification was realized. This two-level approach was increased the accuracy of the classification in all other cases except when 32-component GMMs were used. While the highest improvement of 2.45% was achieved with 64 component GMMs, an improvement of 0.79 was achieved with 256 component GMMs.

scholarly journals GMM-based speaker age and gender classification in Czech and Slovak

2017 ◽  
Vol 68 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jiří Přibil ◽  
Anna Přibilová ◽  
Jindřich Matoušek
Keyword(s):  
Classification Accuracy ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Age And Gender ◽  
Subjective Evaluations ◽  
Listening Tests ◽  
Different Types ◽  
Speech Features ◽  
And Gender

Abstract The paper describes an experiment with using the Gaussian mixture models (GMM) for automatic classification of the speaker age and gender. It analyses and compares the influence of different number of mixtures and different types of speech features used for GMM gender/age classification. Dependence of the computational complexity on the number of used mixtures is also analysed. Finally, the GMM classification accuracy is compared with the output of the conventional listening tests. The results of these objective and subjective evaluations are in correspondence.

scholarly journals Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 374 ◽  
Author(s):  
Sudhanshu Kumar ◽  
Monika Gahalawat ◽  
Partha Pratim Roy ◽  
Debi Prosad Dogra ◽  
Byung-Gyu Kim
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Age Groups ◽  
Modern World ◽  
Support Vector ◽  
Digital Information ◽  
Age And Gender ◽  
And Gender ◽  
The Impact

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

The Influence of Age and Gender on Mandibular Indices among the Libyan Population

2019 ◽  
Vol 3 (2) ◽  
Keyword(s):  
Negative Correlation ◽  
Bone Mineralization ◽  
Age Groups ◽  
T Test ◽  
Chi Square ◽  
Age And Gender ◽  
Significant Difference ◽  
Chi Square Test ◽  
And Gender ◽  
Libyan Population

Radiographic Mandibular Indices serve as easy and relatively cheap tools for evaluating bone mineralization. Objectives: To examine the effect of age and gender on three mandibular indices: the panoramic mandibular index (PMI), the mandibular ratio (MR) and the mandibular cortical index (MCI), among Libyan population. Methods: The three indices were measured on 317 digital (OPGs) of adult humans (155 males, 162 females). The sample was divided into six age groups (from 18-25 years through 56-65 years). The measurements were analyzed for interactions with age and sex, using SPSS (Statistical Package for Social Studies) software version no. 22. The tests employed were two way ANOVA, the unpaired T-test and chi-square test. Results: The mean PMI fluctuated between 0.37 s.d. 0.012 and 0.38 s.d. 0.012. among the sixth age groups. One-way ANOVA statistical test revealed no significant of age on PMI. On the other hand gender variation has effect on PMI, since independent sample t-test disclosed that the difference between the male and female PMI means statistically significant. ANOVA test showed that the means of MR among age groups showed a negative correlation i.e. MR mean declined from 3.01 in 18-25 age groups to 2.7 in 55-65 age groups. In contrary, the gender showed no effect on MR according two sample t-test at p> 0.05. In regards with MCI, statistical analysis showed that it affected by age that is C1 was decreasing by age while C2 and C3 were increased by age. Using chi square test the result indicated that there is a significant difference among the different age group and the two genders in MCI readings. Conclusion: PMI was influenced significantly by age but minimally by the gender. MR is not affected by gender but has a negative correlation with age. MCI is affected by both age and gender

Children’s Lives and Deaths in Merovingian Gaul

2020 ◽  
pp. 185-213
Author(s):  
Émilie Perez
Keyword(s):  
Social Identity ◽  
Age Groups ◽  
New Method ◽  
Recent Analysis ◽  
Age And Gender ◽  
The Past ◽  
And Gender ◽  
Grave Goods

The role of children in Merovingian society has long been downplayed, and the study of their graves and bones has long been neglected. However, during the past fifteen years, archaeologists have shown growing interest in the place of children in Merovingian society. Nonetheless, this research has not been without challenges linked to the nature of the biological and material remains. Recent analysis of 315 children’s graves from four Merovingian cemeteries in northern Gaul (sixth to seventh centuries) allows us to understand the modalities of burial ritual for children. A new method for classifying children into social age groups shows that the type, quality, quantity, and diversity of grave goods were directly correlated with the age of the deceased. They increased from the age of eight and particularly around the time of puberty. This study discusses the role of age and gender in the construction and expression of social identity during childhood in the Merovingian period.

Age and gender differences in socially aversive (“dark”) personality traits

2021 ◽  
pp. 089020702098843
Author(s):  
Johanna Hartung ◽  
Martina Bader ◽  
Morten Moshagen ◽  
Oliver Wilhelm
Keyword(s):  
Personality Traits ◽  
Structural Equation ◽  
Age Groups ◽  
Dark Triad ◽  
Equation Modeling ◽  
Model Parameters ◽  
Age And Gender ◽  
Measurement Models ◽  
And Gender ◽  
Latent Means

The strong overlap of personality traits discussed under the label of “dark personality” (e.g., psychopathy, spitefulness, moral disengagement) endorses a common framework for socially aversive traits over and beyond the dark triad. Despite the rapidly growing research on socially aversive traits, there is a lack of studies addressing age-associated differences in these traits. In the present study ( N = 12,501), we investigated the structure of the D Factor of Personality across age and gender using local structural equation modeling, thereby expressing the model parameters as a quasi-continuous, nonparametric function of age. Specifically, we evaluated loadings, reliabilities, factor (co-)variances, and means across 35 locally weighted age groups (from 20 to 54 years), separately for females and males. Results indicated that measurement models were highly stable, thereby supporting the conceptualization of the D factor independent of age and gender. Men exhibited uniformly higher latent means than females and all latent means decreased with increasing age. Overall, D and its themes were invariant across age and gender. Therefore, future studies can meaningfully pursue causes of mean differences across age and between genders.

scholarly journals Classification of SSVEP-based BCIs using Genetic Algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamideh Soltani ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Keivan Maghooli
Keyword(s):  
Genetic Algorithm ◽  
Dimension Reduction ◽  
Classification Accuracy ◽  
Bayesian Method ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Effective Dimension ◽  
Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

Analysing Temporal Effects on Classification of SAR and Optical Images

2021 ◽  
Author(s):  
Ahmet Batuhan Polat ◽  
Ozgun Akcay ◽  
Fusun Balik Sanli
Keyword(s):  
Rural Areas ◽  
Classification Accuracy ◽  
Winter Season ◽  
Support Vector ◽  
Classification Algorithms ◽  
Sar Images ◽  
Optical Images ◽  
Object Based ◽  
Training Samples

<p>Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy.  As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.</p>

scholarly journals Performance of Children in Phonemic and Semantic Verbal Fluency Tasks

Psico-USF ◽  
2016 ◽  
Vol 21 (2) ◽  
pp. 293-304 ◽  
Author(s):  
Gilmara de Lucena Leite ◽  
Izabel Augusta Hazin Pires ◽  
Laura Carolina Lemos Aragão ◽  
Artemis Paiva de Paula ◽  
Ediana Rossely de Oliveira Gomes ◽  
...  
Keyword(s):  
Explicit Memory ◽  
Verbal Fluency ◽  
Age Groups ◽  
Memory Systems ◽  
Semantic Fluency ◽  
Age And Gender ◽  
Northeast Region ◽  
Semantic Verbal Fluency ◽  
And Gender ◽  
Brazilian Northeast

Abstract This study investigated the performance of children from the Brazilian Northeast region, from 7 to 10 years in phonemic and semantic verbal fluency tasks. The participants were 102 subjects (62 girls and 40 boys) who performed three phonemic and three semantic fluency tasks. The results were submitted to correlational and variance analysis to investigate the influence of the variables age and gender on the subjects performance. There was no effect of gender on the results. Significant contrasts between age groups were found, and better performance was observed on phonemic tasks. Also, the performance in this task changed along development, in contrast to what happened with the semantic fluency. The findings seem to be in accordance to neurodevelopmental aspects, taken into account that explicit memory systems show more precocious maturational course, with earlier consolidation, in comparison to the executive functions and frontal lobes, which go on developing until adult ages.

Reliability Estimates and Age and Gender Comparisons of Expectations of Success in Sex-Typed Activities

1985 ◽  
Vol 7 (4) ◽  
pp. 379-388 ◽  
Author(s):  
Charlotte Sanguinetti ◽  
Amelia M. Lee ◽  
Jack Nelson
Keyword(s):  
Skill Acquisition ◽  
Motor Skill ◽  
Age Groups ◽  
Motor Tasks ◽  
Sex Typing ◽  
Age And Gender ◽  
Motor Skill Acquisition ◽  
Reliability Estimates ◽  
And Gender ◽  
The Stability

The purposes of this study were to determine the stability of estimations of success in masculine, feminine, and gender-neutral motor tasks with subjects of three age groups, and to compare expectancies for success of boys and girls at each of the ages. A total of 90 subjects took part in the study, including 15 males and 15 females randomly selected from the three age groups (grades 1 & 2; grades 6 & 7; and adults). Three activities (football, ballet, and swimming) had been sex-typed in a previous study as masculine, feminine, and neutral, respectively. Subjects were asked to indicate how they would expect to perform on three occasions in all three tasks. Results indicated that all age groups can provide reliable expectations for their success in motor skill acquisition, although the younger children's estimates are slightly less reliable, especially on the first trial. Sex-typing of activities was found to definitely affect the performance estimations in all three age groups. Males' expectancies were higher on the male task and females' expectancies were higher on the female task. The younger children's overall estimates of success were higher than those of the older groups.

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