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Observational studies are based on accurate assessment of human state. Abehavior recognition system that models interlocutors' state in real-time cansignificantly aid the mental health domain. However, behavior recognition fromspeech remains a challenging task since it is difficult to find generalizableand representative features because of noisy and high-dimensional data,especially when data is limited and annotated coarsely and subjectively. DeepNeural Networks (DNN) have shown promise in a wide range of machine learningtasks, but for Behavioral Signal Processing (BSP) tasks their application hasbeen constrained due to limited quantity of data. We propose aSparsely-Connected and Disjointly-Trained DNN (SD-DNN) framework to deal withlimited data. First, we break the acoustic feature set into subsets and trainmultiple distinct classifiers. Then, the hidden layers of these classifiersbecome parts of a deeper network that integrates all feature streams. Theoverall system allows for full connectivity while limiting the number ofparameters trained at any time and allows convergence possible with evenlimited data. We present results on multiple behavior codes in the couples'therapy domain and demonstrate the benefits in behavior classificationaccuracy. We also show the viability of this system towards live behaviorannotations.

Sparsely Connected and Disjointly Trained Deep Neural Networks for Low Resource Behavioral Annotation: Acoustic Classification in Couples' Therapy