Towards Interactive Agents that Infer Emotions from Voice and Context Information

Authors

DOI:

https://doi.org/10.4108/eai.4-9-2017.153054

Keywords:

speech analysis, voice, emotions, context, virtual agents, social skills training

Abstract

Conversational agents are increasingly being used for training of social skills. One of their most important benefits is their ability to understand the user`s emotions, to be able to provide natural interaction with humans. However, to infer a conversation partner’s emotional state, humans typically make use of contextual information as well. This work proposes an architecture to extract emotions from human voice in combination with the context imprint of a particular situation. With that information, a computer system can achieve a more human-like type of interaction. The architecture presents satisfactory results. The strategy of combining 2 algorithms, one to cover ‘common cases’ and another to cover ‘borderline cases’ significantly reduces the percentage of mistakes in classification. The addition of context information also increases the accuracy in emotion inferences.

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Published

04-01-2017

How to Cite

1.
Formolo D, Bosse T. Towards Interactive Agents that Infer Emotions from Voice and Context Information. EAI Endorsed Trans Creat Tech [Internet]. 2017 Jan. 4 [cited 2024 Apr. 18];4(10):e2. Available from: https://publications.eai.eu/index.php/ct/article/view/1531