  This paper proposes a speech emotion recognition method based on speech
features and speech transcriptions (text). Speech features such as Spectrogram
and Mel-frequency Cepstral Coefficients (MFCC) help retain emotion-related
low-level characteristics in speech whereas text helps capture semantic
meaning, both of which help in different aspects of emotion detection. We
experimented with several Deep Neural Network (DNN) architectures, which take
in different combinations of speech features and text as inputs. The proposed
network architectures achieve higher accuracies when compared to
state-of-the-art methods on a benchmark dataset. The combined MFCC-Text
Convolutional Neural Network (CNN) model proved to be the most accurate in
recognizing emotions in IEMOCAP data.
