Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields

Valentin Barrière; Chloé Clavel; Slim Essid

ICASSP 2018accepted0 citations

Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields

Valentin Barrière, Chloé Clavel, Slim Essid

Abstract

In this paper, the main goal is to classify, in a human-agent interaction, the attitude of the user using hidden conditional random fields. This model allows us to capture the dynamics of the interaction in the pairs of speech turns (adjacency pairs) analyzed by our system. High level linguistic features are computed at word level. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the SEMAINE corpus. We obtain a Fl-score of 0.80, labeling using the most probable sequence of hidden states.

BibTeX

@inproceedings{icassp2018_attitudeclassifi,
  title = {Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields},
  author = {Valentin Barrière and Chloé Clavel and Slim Essid},
  booktitle = {ICASSP 2018},
  year = {2018}
}