Emotional annotation of data is important in affective computing for the analysis, recognition, and synthesis of emotions. As raters perceive emotion, they make relative comparisons with what they previously experienced, creating “anchors” that influence the annotations. This unconscious influence of the emotional content of previous stimuli in the perception of emotions is referred to as the affective priming effect. This phenomenon is also expected in annotations conducted with out-of-order segments, a common approach for annotating emotional databases. Can the affective priming effect introduce bias in the labels? If yes, how does this bias affect emotion recognition systems trained with these labels? This study presents a detailed analysis of the affective priming effect and its influence on speech emotion recognition (SER). The analysis shows that the affective priming effect affects emotional attributes and categorical emotion annotations. We observe that if annotators assign an extreme score to previous sentences for an emotional attribute (valence, arousal, or dominance), they will tend to annotate the next sentence closer to that extreme. We conduct SER experiments using the most biased sentences. We observe that models trained on the biased sentences perform the best and have the lowest prediction uncertainty.