Enhancing Emotion Recognition from Speech through Feature Selection

In the present work we aim at performance optimization of a speaker-independent emotion recognition system through speech feature selection process. Specifically, relying on the speech feature set defined in the Interspeech 2009 Emotion Challenge, we studied the relative importance of the individual speech parameters, and based on their ranking, a subset of speech parameters that offered advantageous performance was selected. The affect-emotion recognizer utilized here relies on a GMM-UBM-based classifier. In all experiments, we followed the experimental setup defined by the Interspeech 2009 Emotion Challenge, utilizing the FAU Aibo Emotion Corpus of spontaneous, emotionally coloured speech. The experimental results indicate that the correct choice of the speech parameters can lead to better performance than the baseline one.

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