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An important task in human-computer interaction is to rank speech samples according to their expressive content. A preference learning framework is appropriate for obtaining an emotional rank for a set of speech samples. However, obtaining reliable labels for training a preference learning framework is a challenging task. Most existing databases provide sentence-level absolute attribute scores annotated by multiple raters, which have to be transformed to obtain preference labels. Previous studies have shown that evaluators anchor their absolute assessments on previously annotated samples. Hence, this study proposes a novel formulation for obtaining preference learning labels by only considering annotation trends assigned by a rater to consecutive samples within an evaluation session. The experiments show that the use of the proposed anchor-based ordinal labels leads to significantly better performance than models trained using existing alternative labels. 

The uncertainty in modeling emotions makes speech emotion recognition (SER) systems less reliable. An intuitive way to increase trust in SER is to reject predictions with low confidence. This approach assumes that an SER system is well calibrated, where highly confident predictions are often right and low confident predictions are often wrong. Hence, it is desirable to calibrate the confidence of SER classifiers. We evaluate the reliability of SER systems by exploring the relationship between confidence and accuracy, using the expected calibration error (ECE) metric. We develop a multi-label variant of the post-hoc temperature scaling (TS) method to calibrate SER systems, while preserving their accuracy. The best method combines an emotion co-occurrence weight penalty function, a class-balanced objective function, and the proposed multi-label TS calibration method. The experiments show the effectiveness of our developed multi-label calibration method in terms of ac- curacy and ECE. 

Representation learning is a challenging, but essential task in audiovisual learning. A key challenge is to generate strong cross-modal representations while still capturing discriminative information contained in unimodal features. Properly capturing this information is important to increase accuracy and robustness in audio-visual tasks. Focusing on emotion recognition, this study proposes novel cross-modal ladder networks to capture modality-specific in-formation while building strong cross-modal representations. Our method utilizes representations from a backbone network to implement unsupervised auxiliary tasks to reconstruct intermediate layer representations across the acoustic and visual networks. The skip connections between the cross-modal encoder and decoder provide powerful modality-specific and multimodal representations for emotion recognition. Our model on the CREMA-D corpus achieves high performance with precision, recall, and F1 scores over 80% on a six-class problem. 

Chunk-level speech emotion recognition (SER) is a common modeling scheme to obtain better recognition performance than sentence-level formulations. A key open question is the role of lexical boundary information in the process of splitting a sentence into small chunks. Is there any benefit in providing precise lexi- cal boundary information to segment the speech into chunks (e.g., word-level alignments)? This study analyzes the role of lexical boundary information by exploring alternative segmentation strategies for chunk-level SER. We compare six chunk-level segmentation strategies that either consider word-level alignments or traditional time-based segmentation methods by varying the number of chunks and the duration of the chunks. We conduct extensive experiments to evaluate these chunk-level segmentation approaches using multiples corpora, and multiple acoustic feature sets. The results show a minor contribution of the word-level timing boundaries, where centering the chunks around words does not lead to significant performance gains. Instead, the critical factor to effectively segment a sentence into data chunks is to define the number of chunks according to the number of spoken words in the sentence. 

Modeling cross-lingual speech emotion recognition (SER) has become more prevalent because of its diverse applications. Existing studies have mostly focused on technical approaches that adapt the feature, domain, or label across languages, without considering in detail the similarities be- tween the languages. This study focuses on domain adaptation in cross-lingual scenarios using phonetic constraints. This work is framed in a twofold manner. First, we analyze emotion-specific phonetic commonality across languages by identifying common vowels that are useful for SER modeling. Second, we leverage these common vowels as an anchoring mechanism to facilitate cross-lingual SER. We consider American English and Taiwanese Mandarin as a case study to demonstrate the potential of our approach. This work uses two in-the-wild natural emotional speech corpora: MSP-Podcast (American English), and BIIC-Podcast (Taiwanese Mandarin). The proposed unsupervised cross-lingual SER model using these phonetical anchors outperforms the baselines with a 58.64% of unweighted average recall (UAR). 

The increased ubiquitousness of small smart devices, such as cell- phones, tablets, smart watches and laptops, has led to unique user data, which can be locally processed. The sensors (e.g., microphones and webcam) and improved hardware of the new devices have al- lowed running deep learning models that 20 years ago would have been exclusive to high-end expensive machines. In spite of this progress, state-of-the-art algorithms for facial expression recognition (FER) rely on architectures that cannot be implemented on these devices due to computational and memory constraints. Alternatives involving cloud-based solutions impose privacy barriers that prevent their adoption or user acceptance in wide range of applications. This paper proposes a lightweight model that can run in real-time for image facial expression recognition (IFER) and video facial expression recognition (VFER). The approach relies on a personalization mechanism locally implemented for each subject by fine-tuning a central VFER model with unlabeled videos from a target subject. We train the IFER model to generate pseudo labels and we select the videos with the highest confident predictions to be used for adaptation. The adaptation is performed by implementing a federated learning strategy where the weights of the local model are averaged and used by the central VFER model. We demonstrate that this approach can improve not only the performance on the edge device providing personalized models to the users, but also the central VFER model. We implement a federated learning strategy where the weights of the local models are averaged and used by the central VFER. Within corpus and cross-corpus evaluations on two emotional databases demonstrate that edge models adapted with our personalization strategy achieve up to 13.1% gains in F1-scores. Furthermore, the federated learning implementation improves the mean micro F1-score of the central VFER model by up to 3.4%. The proposed lightweight solution is ideal for interactive user interfaces that preserve the data of the users. 

Speech emotion recognition (SER) is a challenging task due to the limited availability of real-world labeled datasets. Since it is easier to find unlabeled data, the use of self-supervised learning (SSL) has become an attractive alternative. This study proposes new pre-text tasks for SSL to improve SER. While our target application is SER, the proposed pre-text tasks include audio-visual formulations, leveraging the relationship between acoustic and facial features. Our proposed approach introduces three new unimodal and multimodal pre-text tasks that are carefully designed to learn better representations for predicting emotional cues from speech. Task 1 predicts energy variations (high or low) from a speech sequence. Task 2 uses speech features to predict facial activation (high or low) based on facial landmark movements. Task 3 performs a multi-class emotion recognition task on emotional labels obtained from combinations of action units (AUs) detected across a video sequence. We pre-train a network with 60.92 hours of unlabeled data, fine-tuning the model for the downstream SER task. The results on the CREMA-D dataset show that the model pre-trained on the proposed domain-specific pre-text tasks significantly improves the precision (up to 5.1%), recall (up to 4.5%), and F1-scores (up to 4.9%) of our SER system.