Annotating and modeling empathy in spoken conversations

Autor: Morena Danieli, Giuseppe Riccardi, Firoj Alam
Rok vydání: 2018
Předmět:
I.2
FOS: Computer and information sciences
Computer science
Human–Human conversation
media_common.quotation_subject
Behavioural sciences
Context (language use)
Empathy
02 engineering and technology
Space (commercial competition)
computer.software_genre
Theoretical Computer Science
Affective scene
Annotation
Spoken conversation
0202 electrical engineering
electronic engineering
information engineering
Feature (machine learning)
Selection (linguistics)
Emotion
Behavior analysis
Affect
Call center
media_common
Computer Science - Computation and Language
business.industry
I.2.7
05 social sciences
Human-Computer Interaction
020201 artificial intelligence & image processing
Affect (linguistics)
Artificial intelligence
0509 other social sciences
050904 information & library sciences
business
Computation and Language (cs.CL)
computer
Software
Natural language processing
Zdroj: Computer Speech & Language. 50:40-61
ISSN: 0885-2308
Popis: Empathy, as defined in behavioral sciences, expresses the ability of human beings to recognize, understand and react to emotions, attitudes and beliefs of others. The lack of an operational definition of empathy makes it difficult to measure it. In this paper, we address two related problems in automatic affective behavior analysis: the design of the annotation protocol and the automatic recognition of empathy from spoken conversations. We propose and evaluate an annotation scheme for empathy inspired by the modal model of emotions. The annotation scheme was evaluated on a corpus of real-life, dyadic spoken conversations. In the context of behavioral analysis, we designed an automatic segmentation and classification system for empathy. Given the different speech and language levels of representation where empathy may be communicated, we investigated features derived from the lexical and acoustic spaces. The feature development process was designed to support both the fusion and automatic selection of relevant features from high dimensional space. The automatic classification system was evaluated on call center conversations where it showed significantly better performance than the baseline.
Comment: Journal of Computer Speech and Language
Databáze: OpenAIRE
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