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

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

Precise recognition of emotion from speech signals aids in enhancing human–computer
interaction (HCI). The performance of a speech emotion recognition (SER) system depends …

Privacy and security-related concerns are growing as machine learning reaches diverse
application domains. The data holders want to train or infer with private data while exploiting …

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

Significant advances are being made in speech emotion recognition (SER) using deep
learning models. Nonetheless, training SER systems remains challenging, requiring both …

In the realm of consumer technology, Artificial Intelligence (AI)-based Speech Emotion
Recognition (SER) has rapidly gained traction and integration into smart home systems. Its …

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) …

Recent studies have explored using pre-trained embeddings for speech emotion
recognition, achieving comparable performance to conventional methods that rely on low …

Speaker embeddings are ubiquitous, with applications ranging from speaker recognition
and diarization to speech synthesis and voice anonymization. The amount of information …