The 4th Multimodal Sentiment Analysis Challenge (MuSe) focuses on Multimodal Prediction
of Mimicked Emotions, Cross-Cultural Humour, and Personalised Recognition of Affects …

Human emotion understanding is pivotal in making conversational technology mainstream.
We view speech emotion understanding as a perception task which is a more realistic …

We propose a novel cascaded cross-modal transformer (CCMT) that combines speech and
text transcripts to detect customer requests and complaints in phone conversations. Our …

Large pre-trained models are essential in paralinguistic systems, demonstrating
effectiveness in tasks like emotion recognition and stuttering detection. In this paper, we …

This paper presents our solution for the Requests Sub-challenge of the ACM Multimedia
2023 Computational Paralinguistics Challenge. Drawing upon the framework of self …

Speech classification tasks often require powerful language understanding models to grasp
useful features, which becomes problematic when limited training data is available. To attain …

Speech emotion recognition has evolved from research to practical applications. Previous
studies of emotion recognition from speech have focused on developing models on certain …

The ACM Multimedia 2023 ComParE challenge focuses on classification/regression tasks
for spoken customer-agent and emotionally rated conversations. The challenge baseline …

Speech embeddings, fixed-size representations derived from raw audio data, play a crucial
role in diverse machine learning applications. Despite the abundance of speech embedding …

In the past few years, self-supervised learning has revolutionalized automatic speech
recognition. Self-supervised models such as wav2vec2, due to their generalization ability on …