  Recurrent neural network (RNN) are being extensively used over feed-forward
neural networks (FFNN) because of their inherent capability to capture temporal
relationships that exist in the sequential data such as speech. This aspect of
RNN is advantageous especially when there is no a priori knowledge about the
temporal correlations within the data. However, RNNs require large amount of
data to learn these temporal correlations, limiting their advantage in low
resource scenarios. It is not immediately clear (a) how a priori temporal
knowledge can be used in a FFNN architecture (b) how a FFNN performs when
provided with this knowledge about temporal correlations (assuming available)
during training. The objective of this paper is to explore k-FFNN, namely a
FFNN architecture that can incorporate the a priori knowledge of the temporal
relationships within the data sequence during training and compare k-FFNN
performance with RNN in a low resource scenario. We evaluate the performance of
k-FFNN and RNN by extensive experimentation on MediaEval 2016 audio data
("Emotional Impact of Movies" task). Experimental results show that the
performance of k-FFNN is comparable to RNN, and in some scenarios k-FFNN
performs better than RNN when temporal knowledge is injected into FFNN
architecture. The main contributions of this paper are (a) fusing a priori
knowledge into FFNN architecture to construct a k-FFNN and (b) analyzing the
performance of k-FFNN with respect to RNN for different size of training data.
