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Self-Assessed Affect Recognition Using Fusion of Attentional BLSTM and Static Acoustic Features

In this study, we present a computational framework to partici-pate in the Self-Assessed Affect Sub-Challenge in the INTERSPEECH 2018 Computation Paralinguistics Challenge. The goal of this sub-challenge is to classify the valence scores given by the speaker themselves into three different levels, i.e., low, medium, and high. We explore fusion of Bi-directional LSTM with baseline SVM models to improve the recognition accuracy. In speciﬁcs, we extract frame-level acoustic LLDs as input to the BLSTM with a modiﬁed attention mechanism, and separate SVMs are trained using the standard ComParE 16 baseline feature sets with minority class upsampling. These diverse prediction results are then further fused using a decision-level score fusion scheme to integrate all of the developed models. Our proposed approach achieves a 62.94% and 67.04% unweighted average recall (UAR), which is an 6.24% and 1.04% absolute improvement over the best baseline provided by the challenge organizer. We further provide a detailed comparison analysis between different models.