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Most existing audio-text emotion recognition studies have focused on the computational modeling aspects, including strategies for fusing the modalities. An area that has received less attention is understanding the role of proper temporal synchronization between the modalities in the model performance. This study presents a transformer-based model designed with a word-chunk concept, which offers an ideal framework to explore different strategies to align text and speech. The approach creates chunks with alternative alignment strategies with different levels of dependency on the underlying lexical boundaries. A key contribution of this study is the multi-scale chunk alignment strategy, which generates random alignments to create the chunks without considering lexical boundaries. For every epoch, the approach generates a different alignment for each sentence, serving as an effective regularization method for temporal dependency. Our experimental results based on the MSP-Podcast corpus indicate that providing precise temporal alignment information to create the audio-text chunks does not improve the performance of the system. The attention mechanisms in the transformer-based approach are able to compensate for imperfect synchronization between the modalities. However, using exact lexical boundaries makes the system highly vulnerable to missing modalities. In contrast, the model trained with the proposed multi-scale chunk regularization strategy using random alignment can significantly increase its robustness against missing data and remain effective, even under a single audio-only emotion recognition task. The code is available at: https://github.com/winston-lin-wei-cheng/MultiScale-Chunk-Regularization 

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. 

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. 

The field of speech emotion recognition (SER) aims to create scientifically rigorous systems that can reliably characterize emotional behaviors expressed in speech. A key aspect for building SER systems is to obtain emotional data that is both reliable and reproducible for practitioners. However, academic researchers encounter difficulties in accessing or collecting naturalistic, large-scale, reliable emotional recordings. Also, the best practices for data collection are not necessarily described or shared when presenting emotional corpora. To address this issue, the paper proposes the creation of an affective naturalistic database consortium (AndC) that can encourage multidisciplinary cooperation among researchers and practitioners in the field of affective computing. This paper’s contribution is twofold. First, it proposes the design of the AndC with a customizable-standard framework for intelligently-controlled emotional data collection. The focus is on leveraging naturalistic spontaneous record- ings available on audio-sharing websites. Second, it presents as a case study the development of a naturalistic large-scale Taiwanese Mandarin podcast corpus using the customizable- standard intelligently-controlled framework. The AndC will en- able research groups to effectively collect data using the provided pipeline and to contribute with alternative algorithms or data collection protocols. 

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.