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- W4321353452 abstract "This paper presents the estimation of accuracy of different classifiers which are used for the identification of gender with the help of voice signals. The voice signal has rich information about the speaker. The analysis of the voice signals is very vital for accurate and fast identification of gender. In this paper, the Mel Frequency Cepstral Coefficients (MFCCs) are used as an extracted feature of the voice signals of the speakers. MFCCs are the most convenient and reliable feature which sets to configure the gender identification system. Recurrent Neural Network—Bidirectional Long Short-Term Memory (RNN-BiLSTM), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) Gaussian Mixture Model (GMM) and K-Nearest Neighbor (KNN) are utilized as classifiers in this proposed work. In this article, the RNN-BiLSTM classifier has single-layer architecture, while SVM and LDA have a kfold value of 5. The highest accuracy for gender identification is found as 88.66%. The result of the simulations shows that the accuracy of the RNN is always found at a higher value as compared to SVM, LDA, GMM and KNN." @default.
- W4321353452 created "2023-02-20" @default.
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- W4321353452 date "2023-01-01" @default.
- W4321353452 modified "2023-10-07" @default.
- W4321353452 title "Estimation of Accuracy in Gender Identification Based on Voice Signals Using Different Classifiers" @default.
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- W4321353452 doi "https://doi.org/10.1007/978-981-19-6383-4_33" @default.
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