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Tracking Authentic and In-the-wild Emotions using Speech


Talk for the paper accepted at ACII-Asia 2018, which Prof. Schuller is going to present.

Abstract: This first-of-its-kind study aims to track authentic affect representations in-the-wild. We use the `Graz Real-life Affect in the Street and Supermarket (GRAS2)' corpus featuring audiovisual recordings of random participants in non-laboratory conditions. The participants were initially unaware of being recorded. This paradigm enabled us to use a collection of a wide range of authentic, spontaneous and natural affective behaviours. Six raters annotated twenty-eight conversations averaging 2.5 minutes in duration, tracking the arousal and valence levels of the participants. We generate the gold standards through a novel robust Evaluator Weighted Estimator (EWE) formulation. We train Support Vector Regressors (SVR) and Recurrent Neural Networks (RNN) with the low-level-descriptors (LLDs) of the ComParE feature-set in different derived representations including bag-of-audio-words. Despite the challenging nature of this database, a fusion system achieved a highly promising concordance correlation coefficient (CCC) of .372 for arousal dimension, while RNNs achieved a top CCC of .223 in predicting valence, using a bag-of-features representation.

Title: Tracking Authentic and In-the-wild Emotions using Speech
Lecturer: Vedhas Pandit
Date: 14:00 18-05-2018
Building/Room: Eichleitnerstraße 30 / F2 304