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ACII
2015
Springer

Leveraging inter-rater agreement for audio-visual emotion recognition

8 years 7 months ago
Leveraging inter-rater agreement for audio-visual emotion recognition
Abstract—Human expressions are often ambiguous and unclear, resulting in disagreement or confusion among different human evaluators. In this paper, we investigate how audiovisual emotion recognition systems can leverage prototypicality, the level of agreement or confusion among human evaluators. We propose the use of a weighted Support Vector Machine to explicitly model the relationship between the prototypicality of training instances and evaluated emotion from the IEMOCAP corpus. We choose weights of prototypical and non-prototypical instances based on the maximal accuracy of each speaker. We then provide per-speaker analysis to understand specific speech characteristics associated with the information gain of emotion given prototypicality information. Our experimental results show that neutrality, one of the most challenging emotion to recognize, has the highest performance gain from prototypicality information, compared to other emotion classes: Angry, Happy, and Sad. We also sh...
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACII
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