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Semi-supervised learning (SSL) is an appealing approach to resolve generalization problem for speech emotion recognition (SER) systems. By utilizing large amounts of unlabeled data, SSL is able to gain extra information about the prior distribution of the data. Typically, it can lead to better and robust recognition performance. Existing SSL approaches for SER include variations of encoder-decoder model structures such as autoencoder (AE) and variational autoencoders (VAEs), where it is difficult to interpret the learning mechanism behind the latent space. In this study, we introduce a new SSL framework, which we refer to as the DeepEmoCluster framework, for attribute-based SER tasks. The DeepEmoCluster framework is an end-to-end model with mel-spectrogram inputs, which combines a self-supervised pseudo labeling classification network with a supervised emotional attribute regressor. The approach encourages the model to learn latent representations by maximizing the emotional separation of K-means clusters. Our experimental results based on the MSP-Podcast corpus indicate that the DeepEmoCluster framework achieves competitive prediction performances in fully supervised scheme, outperforming baseline methods in most of the conditions. The approach can be further improved by incorporating extra unlabeled set. Moreover, our experimental results explicitly show that the latent clusters have emotional dependencies, enriching the geometric interpretation of the clusters. 

A challenging task in affective computing is to build reliable speech emotion recognition (SER) systems that can accurately predict emotional attributes from spontaneous speech. To increase the trust in these SER systems, it is important to predict not only their accuracy, but also their confidence. An intriguing approach to predict uncertainty is Monte Carlo (MC) dropout, which obtains pre- dictions from multiple feed-forward passes through a deep neural network (DNN) by using dropout regularization in both training and inference. This study evaluates this approach with regression models to predict emotional attribute scores for valence, arousal and dom- inance. The analysis illustrates that predicting uncertainty in this problem is possible, where the performance is higher for samples in the test set with lower uncertainty. The study evaluates uncertainty estimation as a function of the emotional attributes, showing that samples with extreme values have lower uncertainty. Finally, we demonstrate the benefits of uncertainty estimation with reject option, where a classifier can decline to give a prediction when its confi- dence is low. By rejecting only 25% of the test set with the highest uncertainty, we achieve relative performance gains of 7.34% for arousal, 13.73% for valence and 8.79% for dominance. 

Regularization plays a key role in improving the prediction of emotions using attributes such as arousal, valence and dominance. Regularization is particularly important with deep neural networks (DNNs), which have millions of parameters. While previous studies have reported competitive performance for arousal and dominance, the prediction results for valence using acoustic features are significantly lower. We hypothesize that higher regularization can lead to better results for valence. This study focuses on exploring the role of dropout as a form of regularization for valence, suggesting the need for higher regularization. We analyze the performance of regression models for valence, arousal and dominance as a function of the dropout probability. We observe that the optimum dropout rates are consistent for arousal and dominance. However, the optimum dropout rate for valence is higher. To understand the need for higher regularization for valence, we perform an empirical analysis to explore the nature of emotional cues conveyed in speech. We compare regression models with speakerdependent and speaker-independent partitions for training and testing. The experimental evaluation suggests stronger speaker dependent traits for valence. We conclude that higher regularization is needed for valence to force the network to learn global patterns that generalize across speakers.