Abstract
There has been a significant focus on modelling emotion ambiguity in recent years, with advancements made in representing emotions as distributions to capture ambiguity. However, there has been comparatively less effort devoted to the consideration of temporal dependencies in emotion distributions which encodes ambiguity in perceived emotions that evolve smoothly over time. Recognizing the benefits of using constrained dynamical neural ordinary differential equations (CD-NODE) to model time series as dynamic processes, we propose an ambiguity-aware dual-constrained Neural ODE approach to model the dynamics of emotion distributions on arousal and valence. In our approach, we utilize ODEs parameterised by neural networks to estimate the distribution parameters, and we integrate additional constraints to restrict the range of the system outputs to ensure the validity of predicted distributions. We evaluated our proposed system on the publicly available RECOLA dataset and observed very promising performance across a range of evaluation metrics.
Abstract (translated)
在过去的几年里，情感模糊性的建模已经成为了一个重要的研究领域，通过将情感表示为分布来捕捉模糊性，已经取得了很大的进展。然而，在情感分布中考虑时变依赖性的努力相对较少，这会导致对情感的感知在时间上是不确定的。 为了认识到使用约束动态神经 ordinary differential equations（CD-NODE）建模情感分布的时间过程的有利之处，我们提出了一个具有双重约束的神经 ODE 方法来建模情感分布的动力学。在我们的方法中，我们利用由神经网络参数化的 ODE 来估计分布参数，并整合了额外的约束来限制系统输出范围，以确保预测分布的有效性。 我们对所提出的系统在公开可用的 RECOLA 数据集上进行了评估，并观察到在各种评估指标上具有非常出色的表现。
URL
https://arxiv.org/abs/2407.21344
