The aim of this paper is tracking Parkinson's disease (PD) progression based on its symptoms on vocal system using Unified Parkinsons Disease Rating Scale (UPDRS). We utilize a standard speech signal feature set, which contains 6373 static features as functionals of low-level descriptor (LLD) contours, and select the most informative ones using the maximal relevance and minimal redundancy based on correlations (mRMR) criteria. Then, we evaluate performance of Gaussian mixture regression (GMR) and support vector regression (SVR) on estimating the third subscale of UPDRS, i.e., UPDRS: motor subscale (UPDRS-III). Among the most informative features, a list of features are selected after redundancy reduction. The selected features depict that LLDs providing information about spectrum flatness, spectral distribution of energy, and hoarseness of voice are the most important ones for estimating UPDRS-III. Moreover, the most informative statistical functions are related to range, maximum, minimum and standard deviation of LLDs, which is an evidence of the muscle weakness due to the PD. Furthermore, GMR outperforms SVR on compact feature sets while the performance of SVR improves by increasing number of features.
Natural and affective handshakes of two participants define the course of dyadic interaction. Affective states of the participants are expected to be correlated with the nature or type of the dyadic interaction. In this study, we investigate relationship between affective attributes and nature of dyadic interaction. In this investigation we use the JESTKOD database, which consists of speech and full-body motion capture data recordings for dyadic interactions under agreement and disagreement scenarios. The dataset also has affective annotations in activation, valence and dominance (AVD) attributes. We pose the continuous affect recognition problem under agreement and disagreement scenarios of dyadic interactions. We define a statistical mapping using the support vector regression (SVR) from speech and motion modalities to affective attributes with and without the dyadic interaction type (DIT) information. We observe an improvement in estimation of the valence attribute when the DIT is available. Furthermore this improvement sustains even we estimate the DIT from the speech and motion modalities of the dyadic interaction.
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