Improving speech recognition using data augmentation and acoustic model fusion

Deep learning based systems have greatly improved the performance in speech recognition tasks, and various deep architectures and learning methods have been developed in the last few years. Along with that, Data Augmentation (DA), which is a common strategy adopted to increase the quantity of training data, has been shown to be effective for neural network training to make invariant predictions. On the other hand, Ensemble Method (EM) approaches have received considerable attention in the machine learning community to increase the effectiveness of classifiers. Therefore, we propose in this work a new Deep Neural Network (DNN) speech recognition architecture which takes advantage from both DA and EM approaches in order to improve the prediction accuracy of the system. In this paper, we first explore an existing approach based on vocal tract length perturbation and we propose a different DA technique based on feature perturbation to create a modified training data sets. Finally, EM techniques are used to integrate the posterior probabilities produced by different DNN acoustic models trained on different data sets. Experimental results demonstrate an increase in the recognition performance of the proposed system.