Gender Voice Recognition Using Random Forest Recursive Feature Elimination with Gradient Boosting Machines

Zvarevashe, Kudakwashe; Olugbara, Oludayo O.

doi:10.1109/icabcd.2018.8465466

Cited by 28 publications

(21 citation statements)

References 22 publications

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“…It performs random sampling with replacement over a weighted average and reduces classification bias and variance of a decision tree [79]. One of the bagging ensemble algorithms investigated in this study was random decision forest (RDF), which is an extension over bagging that is popularly called random forest [75,82]. RDF can be constituted by making use of bagging based on the CART approach to raise trees [83].…”

Section: Classificationmentioning

confidence: 99%

“…The other bagging ensemble learning algorithms investigated in this study were Bagging with SVM as the base learner (BSVM) and bagging with the multilayer perceptron neural network as the base learner (BMLP). The boosting ensemble algorithms investigated in this study were the gradient boosting machine (GBM), which extends boosting by combining the gradient descent optimization algorithm with boosting technique [75,82,84], and AdaBoost with CART as the base learner (ABC), which is one of the most widely used boosting algorithm to reduce sensitivity to class label noise [79,85]. AdaBoost is an iterative learning algorithm for constructing a strong classifier by enhancing weak classification algorithms and it can improve data classification ability by reducing both bias and variance through continuous learning [81].…”

Section: Classificationmentioning

confidence: 99%

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Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Zvarevashe

Olugbara

2020

Algorithms

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Automatic recognition of emotion is important for facilitating seamless interactivity between a human being and intelligent robot towards the full realization of a smart society. The methods of signal processing and machine learning are widely applied to recognize human emotions based on features extracted from facial images, video files or speech signals. However, these features were not able to recognize the fear emotion with the same level of precision as other emotions. The authors propose the agglutination of prosodic and spectral features from a group of carefully selected features to realize hybrid acoustic features for improving the task of emotion recognition. Experiments were performed to test the effectiveness of the proposed features extracted from speech files of two public databases and used to train five popular ensemble learning algorithms. Results show that random decision forest ensemble learning of the proposed hybrid acoustic features is highly effective for speech emotion recognition.

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Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Zvarevashe

Olugbara

2020

Algorithms

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“…3 Consequently, some of the core visual biometric technologies initially built for person identification have demonstrated the ability to differentiate between genders, with reliable precision. GR can, for example, be achieved using low frequency data from the outline of a human face [4][5][6][7] ; kinematic data from gait analysis 3,8 ; skin texture 9 ; keystroke 10 ; voice; [11][12][13] and speech. [14][15][16][17][18] Recently some group of researchers worked on Parkinson's disease detection and GR using the deep network including pooling and feature extraction methods, to improve on the existing methods on GR.…”

Section: Introductionmentioning

confidence: 99%

Laughter signature, a new approach to gender recognition

Folorunso

Popoola

Asaolu

2020

Engineering Reports

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Gender Recognition (GR) is the process of identifying gender difference by extracting and evaluating features from data existing as images, video, audio, text, and other signals. The task of GR has been achieved using face, keystroke, gait, and speech features. "Laughter" with its intrinsic usefulness for this task is yet to be explored. Laughter is an important paralinguistic feature in human communication. It can change the meaning of speech when triggered by some form of arousal or amusement. Although, laughter can be "acted," but the human natural laughter is a spontaneous reflex response, which reasonably embeds some characteristic peculiarities of the individuals. The human brain has capacity to make this distinction. In this study, spontaneous laughter bouts of 123 volunteers (41 females and 82 males) were recorded. The Dynamic-Average of the Mel frequency Cepstral Coefficient (DA-MFCC) were generated and trained using two conventional and effective machine learning algorithms that have been employed in gender identification. These algorithms are Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) classifiers. Overall accuracies of 87.65% and 86.91% were obtained with GMM and SVM respectively. Therefore, indicating the possibility of using laughter characteristics as signatures for distinguishing between male and female genders. For both classifiers, the use of DA-MFCC reasonably reduced training time. Some of the potential areas of applications of GR include security, health care, marketing, human machine interaction toward enhanced emotion recognition, automatic speaker recognition and forensics.

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“…Random forest (RF) is a versatile and robust ensemble ML algorithm based on decision trees for both classification and regression problems, 29 which can gain accuracy as the trees grow without suffering from overtraining. It has been successfully employed in several fields such as image classification, 30 voice recognition, 31 weather forecast, 32 and material classification. 33 On account of its extraordinary capability, the RF algorithm is utilized to predict the mechanical properties of WS 2 with the help of MD generated training dataset in this work.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enabled Prediction of Mechanical Properties of Tungsten Disulfide Monolayer

et al. 2019

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One of two-dimensional transition metal dichalcogenide materials, tungsten disulfide (WS 2 ), has aroused much research interest, and its mechanical properties play an important role in a practical application. Here the mechanical properties of h-WS 2 and t-WS 2 monolayers in the armchair and zigzag directions are evaluated by utilizing the molecular dynamics (MD) simulations and machine learning (ML) technique. We mainly focus on the effects of chirality, system size, temperature, strain rate, and random vacancy defect on mechanical properties, including fracture strain, fracture strength, and Young’s modulus. We find that the mechanical properties of h-WS 2 surpass those of t-WS 2 due to the different coordination spheres of the transition metal atoms. It can also be observed that the fracture strain, fracture strength, and Young’s modulus decrease when temperature and vacancy defect ratio are enhanced. The random forest (RF) supervised ML algorithm is employed to model the correlations between different impact factors and target outputs. A total number of 3600 MD simulations are performed to generate the training and testing dataset for the ML model. The mechanical properties of WS 2 (i.e., target outputs) can be predicted using the trained model with the knowledge of different input features, such as WS 2 type, chirality, temperature, strain rate, and defect ratio. The mean square errors of ML predictions for the mechanical properties are orders of magnitude smaller than the actual values of each property, indicating good training results of the RF model.

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Gender Voice Recognition Using Random Forest Recursive Feature Elimination with Gradient Boosting Machines

Cited by 28 publications

References 22 publications

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Laughter signature, a new approach to gender recognition

Machine Learning Enabled Prediction of Mechanical Properties of Tungsten Disulfide Monolayer

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