Abstract
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.
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