Name
Abstract | ||
|---|---|---|
In this work we analyze the importance of lexical and acoustic modalities in behavioral expression and perception. We demonstrate that this importance relates to the amount of therapy, and hence communication training, that a person received. It also exhibits some relationship to gender. We proceed to provide an analysis on couple therapy data by splitting the data into clusters based on gender or stage in therapy. Our analysis demonstrates the significant difference between optimal modality weights per cluster and relationship to therapy stage. Given this finding we propose the use of communication-skill aware fusion models to account for these differences in modality importance. The fusion models operate on partitions of the data according to the gender of the speaker or the therapy stage of the couple. We show that while most multimodal fusion methods can improve mean absolute error of behavioral estimates, the best results are given by a model that considers the degree of communication training among the interlocutors.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3242969.3242996 | ICMI |
Keywords | Field | DocType |
Behavior analysis, multimodal fusion, expert fusion | Modalities,Computer science,Fusion,Mean absolute error,Human–computer interaction,Artificial intelligence,Perception,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5692-3 | 0 | 0.34 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shao-Yen Tseng | 1 | 8 | 4.02 |
| Haoqi Li | 2 | 4 | 5.74 |
| Brian R. Baucom | 3 | 152 | 16.36 |
| Georgiou Panayiotis | 4 | 428 | 55.79 |