DNN-based Models for Speaker Age and Gender Classification

Automatic speaker age and gender classification is an active research field due to the continuous and rapid development of applications related to humans' life and health. In this paper, we propose a new method for speaker age and gender classification, which utilizes deep neural networks (DNNs) as feature extractor and classifier. The proposed method creates a model for each speaker. For each test speech utterance, the similarity between the test model and the speaker class models are compared. Two feature sets have been used: Melfrequency cepstral coefficients (MFCCs) and shifted delta cepstral (SDC) coefficients. The proposed model by using the SDC feature set achieved better classification results than that of MFCCs. The experimental results showed that the proposed SDC speaker model + SDC class model outperformed all the other systems by achieving 57.21% overall classification accuracy.