Gil Keren

Convolutional RNN: An enhanced model for extracting features from sequential data.

Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input. We propose a model that enhances this feature extraction process for the case of sequential data, by feeding patches of the data into a recurrent neural network and using the outputs or hidden states of the recurrent units to compute the extracted features. By doing so, we exploit the fact that a window containing a few frames of the sequential data is a sequence itself and this additional structure might encapsulate valuable information. In addition, we allow for more steps of computation in the feature extraction process, which is potentially beneficial as an affine function followed by a non-linearity can result in too simple features. Using our convolutional recurrent layers, we obtain an improvement in performance in two audio classification tasks, compared to traditional convolutional layers.

Convolutional Neural Networks with Data Augmentation for Classifying Speakers' Native Language.

We use a feedforward Convolutional Neural Network to classify speakers’ native language for the INTERSPEECH 2016 Computational Paralinguistic Challenge Native Language SubChallenge, using no specialized features for computational paralinguistics tasks, but only MFCCs with their first and second order deltas. In addition, we augment the training data by replacing the original examples with shorter overlapping samples extracted from them, thus multiplying the number of training examples by almost 40. With the augmented training dataset and enhancements to neural network models such as Batch Normalization, Dropout, and Maxout activation function, we managed to improve upon the challenge baseline by a large margin, both for the development and the test set.