  Acoustic and linguistic analysis for elderly emotion recognition is an
under-studied and challenging research direction, but essential for the
creation of digital assistants for the elderly, as well as unobtrusive
telemonitoring of elderly in their residences for mental healthcare purposes.
This paper presents our contribution to the INTERSPEECH 2020 Computational
Paralinguistics Challenge (ComParE) - Elderly Emotion Sub-Challenge, which is
comprised of two ternary classification tasks for arousal and valence
recognition. We propose a bi-modal framework, where these tasks are modeled
using state-of-the-art acoustic and linguistic features, respectively. In this
study, we demonstrate that exploiting task-specific dictionaries and resources
can boost the performance of linguistic models, when the amount of labeled data
is small. Observing a high mismatch between development and test set
performances of various models, we also propose alternative training and
decision fusion strategies to better estimate and improve the generalization
performance.
