  Despite the enormous interest in emotion classification from speech, the
impact of noise on emotion classification is not well understood. This is
important because, due to the tremendous advancement of the smartphone
technology, it can be a powerful medium for speech emotion recognition in the
outside laboratory natural environment, which is likely to incorporate
background noise in the speech. We capitalize on the current breakthrough of
Recurrent Neural Network (RNN) and seek to investigate its performance for
emotion classification from noisy speech. We particularly focus on the recently
proposed Gated Recurrent Unit (GRU), which is yet to be explored for emotion
recognition from speech. Experiments conducted with speech compounded with
eight different types of noises reveal that GRU incurs an 18.16% smaller
run-time while performing quite comparably to the Long Short-Term Memory
(LSTM), which is the most popular Recurrent Neural Network proposed to date.
This result is promising for any embedded platform in general and will initiate
further studies to utilize GRU to its full potential for emotion recognition on
smartphones.
