Alec Burmania

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We present the MSP-IMPROV corpus, a multimodal emotional database, where the goal is to have control over lexical content and emotion while also promoting naturalness in the recordings. Studies on emotion perception often require stimuli with fixed lexical content, but that convey different emotions. These stimuli can also serve as an instrument to(More)
Manual annotations and transcriptions have an ever-increasing importance in areas such as behavioral signal processing, image processing, computer vision, and speech signal processing. Conventionally, this metadata has been collected through manual annotations by experts. With the advent of crowdsourcing services, the scientific community has begun to(More)
Emotional descriptors collected from perceptual evaluations are important in the study of emotions. Many studies on emotion recognition depend on these labels to train classifiers. The reliability of the emotion descriptors vary with the number and quality of the raters. Conducting perceptual evaluations used to be an expensive and time demanding task,(More)
Affect recognition is a difficult problem that most often relies on human annotated data to train automated systems. As humans perceive emotion differently based on personality, cognitive state and past experiences, it is important to collect rankings from multiple individuals to assess the emotional content in corpora, which are later aggregated with rules(More)
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