Deep convolutional neural networks for acoustic modeling in low resource languages
William Chan, Ian Richard Lane · 2015
Convolutional Neural Networks (CNNs) have demonstrated powerful acoustic modelling capabilities due to their ability to account for structural locality in the feature space; and in recent works CNNs have been shown to often outperform fully connected Deep Neural Networks (DNNs) on TIMIT and LVCSR. In this paper, we perform a detailed empirical study of CNNs under the low resource condition, wherein we only have 10 hours of training data. We find a two dimensional convolutional structure performs the best, and emphasize the importance to consider time and spectrum in modelling acoustic patterns. We report detailed error rates across a wide variety of model structures and show CNNs consistently outperform fully connected DNNs for this task.