Using the influence model coefficients and the random walk to predict emotional interactions in parent-child conversations

Stolar, M, Lech, M and Burnett, I 2014, 'Using the influence model coefficients and the random walk to predict emotional interactions in parent-child conversations', in Proceedings of the 8th International Conference on Signal Processing and Communication Systems (ICSPCS 2014), Gold Coast, Australia, 15-17 December 2014, pp. 61-67.


Document type: Conference Paper
Collection: Conference Papers

Title Using the influence model coefficients and the random walk to predict emotional interactions in parent-child conversations
Author(s) Stolar, M
Lech, M
Burnett, I
Year 2014
Conference name ICSPCS 2014
Conference location Gold Coast, Australia
Conference dates 15-17 December 2014
Proceedings title Proceedings of the 8th International Conference on Signal Processing and Communication Systems (ICSPCS 2014)
Publisher IEEE
Place of publication United States
Start page 61
End page 67
Total pages 7
Abstract This study introduces an interactive random walk as a new method for predicting sequences of four different construct states (positive emotion, negative emotion, neutral emotion and silence) of speakers in parent-child conversations. The proposed approach used the emotional transition probability arrays and the Influence Model (IM) coefficients to support the interacting random walk predictions. The interactive random walk was applied to generate sequences of speakers' states using higher order emotional transition probabilities. The new approach was tested on 63 different parent-child conversations conducted in naturalistic (not-acted) way. The prediction outcomes were visualized using the 2D random walk on a graph approach. The prediction quality was measured using the relative error between the actual and the predicted transition probabilities as well as, the error between the actual and the predicted end-point position on the 2D graph of emotional states. A comparison between the proposed random walk supported by the IM coefficients and the classical approach without the IM coefficients showed that proposed method generally offers improved results in terms of the prediction error and the endpoint position but at the cost of slower convergence rates.
Subjects Signal Processing
Keyword(s) directed graphs
emotion recognition
probability
rough set theory
social sciences computing
DOI - identifier 10.1109/ICSPCS.2014.7021130
Copyright notice © 2014 IEEE
ISBN 9781479952564
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