Speech Emotion Recognition under White Noise

Speaker‘s emotional states are recognized from speech signal with Additive white Gaussian noise
(AWGN). The influence of white noise on a typical emotion recogniztion system is studied. The emotion
classifier is implemented with Gaussian mixture model (GMM). A Chinese speech emotion database
is used for training and testing, which includes nine emotion classes (e.g. happiness, sadness, anger,
surprise, fear, anxiety, hesitation, confidence and neutral state). Two speech enhancement algorithms
are introduced for improved emotion classification. In the experiments, the Gaussian mixture model is
trained on the clean speech data, while tested under AWGN with various signal to noise ratios (SNRs).
The emotion class model and the dimension space model are both adopted for the evaluation of the
emotion recognition system. Regarding the emotion class model, the nine emotion classes are classified.
Considering the dimension space model, the arousal dimension and the valence dimension are classified
into positive regions or negative regions. The experimental results show that the speech enhancement
algorithms constantly improve the performance of our emotion recognition system under various SNRs,
and the positive emotions are more likely to be miss-classified as negative emotions under white noise
environment.