There has been increasing attention drawn to modelling interrater ambiguity in Continuous Emotion Recognition (CER) systems using probability distributions for arousal and valence.However, the relationship between modelling label ambiguity and robustness to noise, and more broadly, the impact of realworld noise on CER systems remains insufficiently explored.In this study, we argue that incorporating inter-rater ambiguity during training can regularize the noise response, leading to noise robustness.To this end, we propose a novel loss function that incorporates inter-rater ambiguity into model training.Experiments conducted on the RECOLA dataset demonstrate that our proposed method achieves a maximum Concordance Correlation Coefficient (CCC) improvement of 0.117 and 0.077 for mean and standard deviation predictions, respectively, across all noise conditions.We further integrate traditional noisy augmentation strategies with our proposed method and observe promising results.

Despite of efforts made to model emotion ambiguity and develop ambiguity aware emotion prediction systems, there is a need for a quantitative and interpretable measure of the accuracy of such systems, regardless of recent advances in representing emotion ambiguity through probability distributions. In this paper, we propose a novel measure called the “Belief Mismatch Coefficient (BMC) that quantifies the differences in the belief that emotional states are perceived from certain regions within the arousal/valence space when comparing a predicted distribution to an underlying distribution inferred from ground truth ratings. The proposed metric is validated using simulated labels to demonstrate its effectiveness in quantifying various prediction errors. Furthermore, it is extended to real-case emotion prediction systems using two state-of-the-art modeling techniques on the RECOLA dataset. The experimental results confirm that the proposed metric can efficiently capture and differentiate between various prediction errors, while also offering insights into the predictions. Moreover, it demonstrates significant advantages in capturing a comprehensive view of the predicted distribution compared to traditional metrics such as Concordance Correlation Coefficients.

weA number of machine learning applications involve time series prediction, and in some cases additional information about dynamical constraints on the target time series may be available. For instance, it might be known that the desired quantity cannot change faster than some rate or that the rate is dependent on some known factors. However, incorporating these constraints into deep learning models, such as recurrent neural networks, is not straightforward. In this paper, we propose constrained dynamical neural ordinary differential equation (CD-NODE) models, which treat the desired time series as a dynamic process that can be described by an ODE. CD-NODEs model the rate of change of the time series as a function of both itself and the current input features, parameterised as a neural network. We explore the effect of constraining the dynamics of the model by placing explicit restrictions on the rate of change. The proposed model is evaluated on speech-based continuous emotion prediction, where such dynamical constraints are expected, using the publicly available RECOLA dataset. Results suggest that the model achieves performances comparable with the state-of-the-art despite using significantly fewer parameters. Additional analyses reveal that imposing these constraints on the model leads to faster convergence and better performance, especially with smaller training data sets.

There is growing interest in affective computing for the representation and prediction of emotions along ordinal scales. However, the term ordinal emotion label has been used to refer to both absolute notions such as low or high arousal, as well as relation notions such as arousal is higher at one instance compared to another. In this paper, we introduce the terminology absolute and relative ordinal labels to make this distinction clear and investigate both with a view to integrate them and exploit their complementary nature. We propose a Markovian framework referred to as Dynamic Ordinal Markov Model (DOMM) that makes use of both absolute and relative ordinal information, to improve speech based ordinal emotion prediction. Finally, the proposed framework is validated on two speech corpora commonly used in affective computing, the RECOLA and the IEMOCAP databases, across a range of system configurations. The results consistently indicate that integrating relative ordinal information improves absolute ordinal emotion prediction.

When continuous emotion labelling of natural (non-acted) data is desired, it is typically collected from multiple annotators. However, most automatic emotion recognition systems trained on such data ignore disagreement between annotators and only models the average rating, despite the observation that the degree of disagreement would reflect the ambiguity and subtlety in every expression of emotions. In this paper, we propose a novel Sequential Monte Carlo framework that models the perceived emotion as time-varying distributions that allows for ambiguity to be incorporated. Additionally, we present alternative measures that consider both the similarity of prediction to the multiple labels, as well as whether the degree of ambiguity in the prediction and labels. The proposed system was validated on the publicly available RECOLA dataset.