Study on Speaker-Independent Emotion Recognition from Speech on Real-World Data

In the present work we report results from on-going research activity in the area of speaker-independent emotion recognition. Experimentations are performed towards examining the behavior of a detector of negative emotional states over non-acted/acted speech. Furthermore, a score-level fusion of two classifiers on utterance level is applied, in attempt to improve the performance of the emotion recognizer. Experimental results demonstrate significant differences on recognizing emotions on acted/real-world speech.

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