Speech-to-Gender Recognition Based on Machine Learning Algorithms

Hizlisoy, Serhat; ÇOLAKOĞLU>, Emel; Arslan, Recep Sinan

doi:10.18100/ijamec.1221455

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…In this study, for the 1st and 2nd layers to design the ensemble model, 8 different classifiers were used, namely Random Forest, Logistic Regression, Multilayer Perceptron, Support Vector Classifier, K-nearest neighbor, XGB, Gaussian Naïve Bayes, and Decision Tree, which have different architectural structures and are widely used to solve various problems [25][26][27]. After this, each classifier was tested independently of each other.…”

Section: Classifier Selection For Ensemble Modelmentioning

confidence: 99%

Suç Kategorisi Tespiti için Yığınlama Topluluk Öğrenimi Modeli Kullanan Çatı Tasarımı

ARSLAN,

DÜLGEROĞLU

2023

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Crime refers to an action legally defined as harmful to society, and it is important to understand the type of crime to prevent these actions. However, crime can occur at any time and place, making it difficult to predict. Data generated based on previously committed crimes contributes to overcoming this difficulty. This study proposes a novel model for classifying criminal activities using a Doc2Vec that can cause a numerical representation of texts regardless of length and a stacking ensemble model that includes 8 different machine-learning models. Unlike the literature, the model processes the features as text and converts them into vectors rather than categorically. In this way, it enables using features that cannot be used in the literature. The proposed model is tested using a distributed online competition database, Francisco Crime Classification, which contains crimes committed over 12 years. An accuracy value of 99.28% was obtained for the 15 crime categories with the highest crime records, while precision, recall, and f-score values were 99.18%, 99.38%, and 99.20%, respectively. With cross-validation (k=10), 99.80% performance was achieved with a std. value of 0.001. These performance values are higher than those of all the studies in the literature using categorical feature structures. The results show that converting criminal activity reports, which contain text-based features, into vectors that can be processed with natural language processing techniques such as Doc2vec instead of using them categorically in model training can directly contribute to the classification performance and provide a more efficient model with less preprocessing.

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Section: Classifier Selection For Ensemble Modelmentioning

confidence: 99%

Suç Kategorisi Tespiti için Yığınlama Topluluk Öğrenimi Modeli Kullanan Çatı Tasarımı

ARSLAN,

DÜLGEROĞLU

2023

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Gender Recognition Based on the Stacking of Different Acoustic Features

Yücesoy

2024

Applied Sciences

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A speech signal can provide various information about a speaker, such as their gender, age, accent, and emotional state. The gender of the speaker is the most salient piece of information contained in the speech signal and is directly or indirectly used in many applications. In this study, a new approach is proposed for recognizing the gender of the speaker based on the use of hybrid features created by stacking different types of features. For this purpose, four different features, namely Mel frequency cepstral coefficients (MFCC), Mel scaled power spectrogram (Mel Spectrogram), Chroma, Spectral contrast (Contrast), and Tonal Centroid (Tonnetz), and twelve hybrid features created by stacking these features were used. These features were applied to four different classifiers, two of which were based on traditional machine learning (KNN and LDA) while two were based on the deep learning approach (CNN and MLP), and the performance of each was evaluated separately. In the experiments conducted on the Turkish subset of the Common Voice dataset, it was observed that hybrid features, created by stacking different acoustic features, led to improvements in gender recognition accuracy ranging from 0.3 to 1.73%.

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Automatic Age and Gender Recognition Using Ensemble Learning

Yücesoy

2024

Applied Sciences

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The use of speech-based recognition technologies in human–computer interactions is increasing daily. Age and gender recognition, one of these technologies, is a popular research topic used directly or indirectly in many applications. In this research, a new age and gender recognition approach based on the ensemble of different machine learning algorithms is proposed. In the study, five different classifiers, namely KNN, SVM, LR, RF, and E-TREE, are used as base-level classifiers and the majority voting and stacking methods are used to create the ensemble models. First, using MFCC features, five base-level classifiers are created and the performance of each model is evaluated. Then, starting from the one with the highest performance, these classifiers are combined and ensemble models are created. In the study, eight different ensemble models are created and the performances of each are examined separately. The experiments conducted with the Turkish subsection of the Mozilla Common Voice dataset show that the ensemble models increase the recognition accuracy, and the highest accuracy of 97.41% is achieved with the ensemble model created by stacking five classifiers (SVM, E-TREE, RF, KNN, and LR). According to this result, the proposed ensemble model achieves superior accuracy compared to similar studies in recognizing age and gender from speech signals.

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Speech-to-Gender Recognition Based on Machine Learning Algorithms

Cited by 3 publications

References 20 publications

Suç Kategorisi Tespiti için Yığınlama Topluluk Öğrenimi Modeli Kullanan Çatı Tasarımı

Suç Kategorisi Tespiti için Yığınlama Topluluk Öğrenimi Modeli Kullanan Çatı Tasarımı

Gender Recognition Based on the Stacking of Different Acoustic Features

Automatic Age and Gender Recognition Using Ensemble Learning

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