Name
Abstract | ||
|---|---|---|
Arousal is essential in understanding human behavior and decision-making. In this work, we present a multimodal arousal rating framework that incorporates minimal set of vocal and non-verbal behavior descriptors. The rating framework and fusion techniques are unsupervised in nature to ensure that it can be readily-applicable and interpretable. Our proposed multimodal framework improves correlation to human judgment from 0.66 (vocal-only) to 0.68 (multimodal); analysis shows that the supervised fusion framework does not improve correlation. Lastly, an interesting empirical evidence demonstrates that the signal-based quantification of arousal achieves a higher agreement with each individual rater than the agreement among raters themselves. This further strengthens that machine-based rating is a viable way of measuring subjective humans' internal states through observing behavior features objectively. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICASSP.2015.7178982 | 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
behavioral signal processing, affective computing, arousal rating, multimodal signal processing | Arousal,Pattern recognition,Computer science,Robustness (computer science),Human judgment,Unsupervised learning,Correlation,Artificial intelligence,Affective computing | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei-Chen Chen | 1 | 10 | 3.89 |
| Po-Tsun Lai | 2 | 3 | 0.77 |
| Yu Tsao | 3 | 208 | 50.09 |
| Chi-Chun Lee | 4 | 654 | 49.41 |