Spoken Letter Recognition using Deep Convolutional Neural Networks on Sparse and Dissimilar Data

Abstract

The applications of Machine Learning and Neural Networks (NN) are nearly unlimited and the application of artificial intelligence has gained much interest in recent years, because of their great performance in various tasks. Deep Convolutional Neural Networks (CNNs) are a state-of-the-art technique for visual recognition in image- and video data. However, the application range of a specific CNN is very limited, because the CNN is adapted for a specific task with an exclusive dataset for training. It needs to be rebuilt from scratch when the input- or output parameters are just slightly changing, including the collection of a new dataset for training. To reduce those cost and time expensive issues, transfer learning can be beneficial, where the outcome of an already pre-trained Neural Network, the source data, is fitted to the target dataset of a new task. In the case of object recognition, there are several use cases where pre-trained Deep CNNs are applied. But those Deep CNNs can not only be used for visual recognition. In this work the approach is made, to use transfer learning on DCNNs for spoken letter recognition, although the target data is very dissimilar from the source data, to show the range of application for transfer learning. Moreover, this application is trained with a very small dataset.