  Deep convolutional neural networks are being actively investigated in a wide
range of speech and audio processing applications including speech recognition,
audio event detection and computational paralinguistics, owing to their ability
to reduce factors of variations, for learning from speech. However, studies
have suggested to favor a certain type of convolutional operations when
building a deep convolutional neural network for speech applications although
there has been promising results using different types of convolutional
operations. In this work, we study four types of convolutional operations on
different input features for speech emotion recognition under noisy and clean
conditions in order to derive a comprehensive understanding. Since affective
behavioral information has been shown to reflect temporally varying of mental
state and convolutional operation are applied locally in time, all deep neural
networks share a deep recurrent sub-network architecture for further temporal
modeling. We present detailed quantitative module-wise performance analysis to
gain insights into information flows within the proposed architectures. In
particular, we demonstrate the interplay of affective information and the other
irrelevant information during the progression from one module to another.
Finally we show that all of our deep neural networks provide state-of-the-art
performance on the eNTERFACE'05 corpus.
