Automatic Spoken Language Identification using Convolutional Neural Networks

Automatic Spoken Language Identification systems classify the spoken language automatically and can be used in many tasks, for example, to support Automatic Speech Recognition or Video Recommendation systems. In this work, we propose an automatic language identification model obtained through a Convolutional Neural Network trained over audio spectrograms on Portuguese, English and Spanish languages. The audio for the model training was obtained through audiobooks and different corpora for speech recognition systems. The audios were used to generate instances having five seconds each. We addressed the limitation of having few speakers in our dataset with simple data augmentation techniques such as speed and pitch changing on the original instances to increase the size of the dataset. The proposed model was optimized with a random hyperparameter search which provided a final model able to identify the proposed languages with 83% of accuracy on a new, unseen test data, made with audios from different sources.