Speeding-up neural network training using sentence and frame selection

Training Artificial Neural Networks (ANNs) with large amounts of speech data is a time intensive task due to the intrinsically sequential nature of the back-propagation algorithm. This paper presents an approach for training ANNs using sentence and frame selection. The goal is to speed-up the training process, and to balance the phonetic coverage of the selected frames, trying to mitigate the classification problems related to the prior probabilities of the individual phonetic classes. These techniques, together with a three-step training approach and software optimizations, reduced by an order of magnitude the training time of our models.