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Various applications depend on speakers' age and gender to satisfy their customer needs. The age and gender information of a speaker are concealed in the speech signal, which is vary in time duration and is exposed to background noise. These effects make the speakers' age and gender prediction one of the most challenging problems in the field of speech processing. Recently, remarkable developments...
Speaker's age and gender classification is one of the most challenging problems in the field of acoustic recognition. Although many studies have been done to obtain better results, the classification accuracies are still not satisfactory. Motivated by the success in the deep learning techniques in speech processing field, we developed a DNN architecture to classify speakers' age and gender. In this...
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