Abstract
In this study we present a novel emotional speech corpus, consisting of dialog that was extracted from an animated film. This type of corpus presents an interesting compromise between the sparsity of emotion found in spontaneous speech, and the contrived emotion found in speech acted solely for research purposes. The dialog was segmented into 453 short units and judged for emotional content by native and non-native English speakers. Emotion was rated on two scales: Activation and Valence. Acoustic analysis gave a comprehensive set of 100 features covering F0, intensity, voice quality and spectrum. We found that Activation is more strongly correlated to our acoustic features than Valence. Activat-ion was correlated to several types of features, whereas Valence was correlated mainly to intensity related features. Further, ANOVA analysis showed some interesting contrasts between the two scales, and interesting differences in the judgments of native vs. non-native English speakers.
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Amir, N., Cohen, R. (2007). Characterizing Emotion in the Soundtrack of an Animated Film: Credible or Incredible?. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2007. Lecture Notes in Computer Science, vol 4738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74889-2_14
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DOI: https://doi.org/10.1007/978-3-540-74889-2_14
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