Speech Recognition Using Convolutional Neural Networks on Small Training Sets

Abstract

The algorithm of separate words automatic recognition based on convolutional neural networks is developed and presented in this paper. Distinctive feature of this algorithm is the training on sets consisting of only hundreds or thousands of samples. Therefore, important problem is the selection of optimal architecture for neural network, which was firstly proposed and tested. After that, four different cases for recognition were researched: speaker-dependent recognition without noise, speaker-independent recognition without noise, speaker-dependent recognition with noise, speaker-independent recognition without noise. Finally, we analyse the experiment results that showed good results for all cases of interest.