Speech emotion recognition (SER) is traditionally performed using merely acoustic
information. Acoustic features, commonly are extracted per frame, are mapped into emotion …

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 combining linguistic content and audio signals in the dialog is a
challenging task. Nevertheless, previous approaches have failed to explore emotion cues in …

Alongside acoustic information, linguistic features based on speech transcripts have been
proven useful in Speech Emotion Recognition (SER). However, due to the scarcity of …

Spoken question answering (SQA) requires fine-grained understanding of both spoken
documents and questions for the optimal answer prediction. In this paper, we propose novel …

Automated multimodal emotion recognition has become an emerging but challenging
research topic in the fields of affective learning and sentiment analysis. The existing works …

Sentiment analysis is a challenging but valuable research topic in affective computing. It can
improve the quality of various real-world applications, including financial market prediction …

Within a single speech emotion corpus, deep neural networks have shown decent
performance in speech emotion recognition. However, the performance of the emotion …

In this paper, we study different approaches for classifying emotions from speech using
acoustic and text-based features. We propose to obtain contextualized word embeddings …

Representation learning for speech emotion recognition is challenging due to labeled data
sparsity issue and lack of gold-standard references. In addition, there is much variability from …