Journal
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
Volume 8, Issue 1, Pages 81-94Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2015.2510625
Keywords
Behavioral coding; behavioral signal processing; couple therapy; multi-modal signal processing; multiple instance learning
Funding
- National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0911009] Funding Source: National Science Foundation
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We propose a computational methodology for automatically estimating human behavioral patterns using the multiple instance learning (MIL) paradigm. We describe the incremental diverse density algorithm, a particular formulation of multiple instance learning, and discuss its suitability for behavioral coding. We use a rich multi-modal corpus comprised of chronically distressed married couples having problem-solving discussions as a case study to experimentally evaluate our approach. In the multiple instance learning framework, we treat each discussion as a collection of short-term behavioral expressions which are manifested in the acoustic, lexical, and visual channels. We experimentally demonstrate that this approach successfully learns representations that carry relevant information about the behavioral coding task. Furthermore, we employ this methodology to gain novel insights into human behavioral data, such as the local versus global nature of behavioral constructs as well as the level of ambiguity in the expression of behaviors through each respective modality. Finally, we assess the success of each modality for behavioral classification and compare schemes for multimodal fusion within the proposed framework.
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