Age prediction by voice using deep learning

Abstract

One of the main topics in artificial intelligence is the speech characterization. Moreover, it is a field of study with the minimal scope when the Catalan language is involved in. In this project, we try to perform an age classification by decades firstly in the Catalan CommonVoice Dataset and then add the Spanish Dataset and English Dataset to have more data. To reach our purpose Deep Learning techniques are used to implement the classifier. The most common backbones are used such as Resnet and VGG. Furthermore, we use an attention encoder to encode the Mel-Spectrogram features. In contrast to statistical pooling methods like average pooling, Attention Pooling layers and various Attention Mechanisms are used in all backbones to perform pooling and reduce the dimensionality of the feature vector derived from the Front-End architecture. In this study, we will compare two different models, the first with an AM-Softmax in the final layer and the other with an AM-Softmax combined with Ordinal Regression.