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Behavior Computing
Speech and Language
Analyzing the Effect of Affective Priming on Emotional Annotations
Abstract
In the field of affective computing, emotional an- notations are highly important for both the recognition and synthesis of human emotions. Researchers must ensure that these emotional labels are adequate for modeling general human perception. An unavoidable part of obtaining such labels is that human annotators are exposed to known and unknown stimuli before and during the annotation process that can affect their perception. Emotional stimuli cause an affective priming effect, which is a pre-conscious phenomenon in which previous emotional stimuli affect the emotional perception of a current target stimulus. In this paper, we use sequences of emotional annotations during a perceptual evaluation to study the effect of affective priming on emotional ratings of speech. We observe that previous emotional sentences with extreme emotional content push annotations of current samples to the same extreme. We create a sentence-level bias metric to study the effect of affective priming on speech emotion recognition (SER) modeling. The metric is used to identify subsets in the database with more affective priming bias intentionally creating biased datasets. We train and test SER models using the full and biased datasets. Our results show that although the biased datasets have low inter- evaluator agreements, SER models for arousal and dominance trained with those datasets perform the best. For valence, however, the models trained with the less-biased datasets perform the best.
Figures
Average difference between current ratings and the expected label for each sentence.
Average difference between current ratings and the expected label for each sentence.
Keywords
Affective Computing | Emotional Annotations | Affective Priming | Emotional Attributes | Speech Emotion Recognition
Conference
ACII 2023
2023 11th International Conference on Affective Computing and Intelligent Interaction (ACII)
Publisher
IEEE