New transformed features generated by deep bottleneck extractor and a GMM-UBM classifier for speaker age and gender classification.

Speaker age and gender classification is one of the most challenging problems in speech signal processing. Recently with developing technologies, identifying speaker age and gender information has become a necessity for speaker verification and identification systems such as identifying suspects in criminal cases, improving human-machine interaction, and adapting music for awaiting people queue. Despite the intensive studies that have been conducted to extract descriptive and distinctive features, the classification accuracies are still not satisfactory. In this work, a model for generating bottleneck features from a deep neural network and a Gaussian Mixture Model-Universal Background Model (GMM-UBM) classifier are proposed for speaker age and gender classification problem. Deep neural network with a bottleneck layer is trained in an unsupervised manner for calculating the initial weights between layers. Then, it is trained and tuned in a supervised manner to generate transformed mel-frequency cepstral coefficients (T-MFCCs). The GMM-UBM is used to build a GMM model for each class, and the models are used to classify speaker age and gender. Age-annotated database of German telephone speech (aGender) is used to evaluate the proposed classification system. The newly generated T-MFCCs have shown potential to achieve significant classification improvements in speaker age and gender classification by using the GMM-UBM classifier. The proposed classification system achieved an overall accuracy of 57.63%. The highest accuracy is calculated as 72.97% for adult female speakers.