The determination of the age and gender of the speaker of the speech signal is an interesting topic in the interaction between human-machine. Speech signal has a variety of applications ranging from speech analyses to allocate human-machine interactions. This paper aims to conduct a comparative study of age and gender classification algorithms applied to the speech signal. Comparison of experimental results of different sources of voices for speakers of different languages and methods of miscellaneous classification such as Bayes classifier, neural network, support vector machines, K-nearest neighbor, gaussien mixture model and hybrid method based on weighted analysis of a directed non-negative matrix and a neural network with a general recession as well as some deep learning methods, is done in order to show different results to classify the age and gender of the speaker when processing the speech signal. The study showed that methods and algorithms of deep learning have excelled in providing accuracy ratios higher than other methods, and it shows that the hybridization of two or more classification methods increases the accuracy level of the results.
Estimating the age and gender of the speaker has gained great importance in recent years due to its necessity in various commercial, medical and forensic applications. This work estimates the speakers gender and ages in small range of years where every ten years has been divided into two subcategories for a span of years extending from teens to sixties. A system of speaker age and gender estimation uses Mel Frequency Cepstrum Coefficient (MFCC) as a features extraction method, and Bidirectional Long-Short Term Memory (BiLSTM) as a classification method. Two models of two deep neural networks were building, one for speaker age estimation, and the other for speaker gender estimation. The experimental results show that the deep neural network model of age estimation achieves 94.008 % as accuracy rate, while the deep neural network model of gender estimation achieves 90.816% as accuracy rate.
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