Deep Learning for Continuous Multiple Time Series Annotations

Abstract

Learning from multiple annotations is an increasingly important research topic. Compared with conventional classification or regression problems, it faces more challenges because time-continuous annotations would result in noisy and temporal lags problems for continuous emotion recognition. In this paper, we address the problem by deep learning for continuous multiple time series annotations. We attach a novel crowd layer to the output layer of basic continuous emotion recognition system, which learns directly from the noisy labels of multiple annotators with end-to-end manner. The inputs of the system are multimodal features and the targets are multiple annotations, with the intention of learning an annotator-specific mapping. Our proposed method considers the ground truth as latent variables and multiple annotations are variant of ground truth by linear mapping. The experimental results show that our system can achieve superior performance and capture the reliabilities and biases of different annotators.