Sentiment analysis (SA) aims to understand the attitudes and views of opinion holders with
computers. Previous studies have achieved significant breakthroughs and extensive …

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

Emotion recognition datasets are relatively small, making the use of the more sophisticated
deep learning approaches challenging. In this work, we propose a transfer learning method …

With the rapid development of social media and human–computer interaction, multimodal
emotion recognition in conversations (MERC) tasks have begun to receive widespread …

Speech emotion recognition (SER) has become a crucial topic in the field of human–
computer interactions. Feature representation plays an important role in SER, but there are …

Progress in speech processing has been facilitated by shared datasets and benchmarks.
Historically these have focused on automatic speech recognition (ASR), speaker …

With the release of increasing open-source emotion recognition datasets on social media
platforms and the rapid development of computing resources, multimodal emotion …

Generic pre-trained speech and text representations promise to reduce the need for large
labeled datasets on specific speech and language tasks. However, it is not clear how to …

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) …

This paper describes our submission to the ADreSSo Challenge, which focuses on the
problem of automatic recognition of Alzheimer's Disease (AD) from speech. The audio …