Advances in computer science and data-analytic methods are driving a new era in mental
health research and application. Artificial intelligence (AI) technologies hold the potential to …

Speech-centric machine learning systems have revolutionized a number of leading
industries ranging from transportation and healthcare to education and defense …

Recent advances in transformer-based architectures have shown promise in several
machine learning tasks. In the audio domain, such architectures have been successfully …

Speech emotion recognition (SER) as a Machine Learning (ML) problem continues to
garner a significant amount of research interest, especially in the affective computing …

Emotion recognition technologies, while critiqued for bias, validity, and privacy invasion,
continue to be developed and applied in a range of domains including in high-stakes …

Artificial intelligence (AI) reconfigures work and organization, while work and organization
shape AI. In this special issue, we explore these mutual transformations and how they play …

Large, pre-trained neural networks consisting of self-attention layers (transformers) have
recently achieved state-of-the-art results on several speech emotion recognition (SER) …

The substantial growth of Internet-of-Things technology and the ubiquity of smartphone
devices has increased the public and industry focus on speech emotion recognition (SER) …

Despite debates about emotion artificial intelligence's (EAI) validity, legality, and social
consequences, EAI is increasingly present in the high stakes context of hiring, with potential …

Many recent studies have focused on fine-tuning pretrained models for speech emotion
recognition (SER), resulting in promising performance compared to traditional methods that …