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

Attention is an important mechanism that can be employed for a variety of deep learning
models across many different domains and tasks. This survey provides an overview of the …

Emotion recognition in conversation is a crucial topic for its widespread applications in the
field of human-computer interactions. Unlike vanilla emotion recognition of individual …

This work reviews the state of the art in multimodal speech emotion recognition
methodologies, focusing on audio, text and visual information. We provide a new …

Self-supervised pre-trained features have consistently delivered state-of-art results in the
field of natural language processing (NLP); however, their merits in the field of speech …

Speech emotion recognition is a challenging task, and extensive reliance has been placed
on models that use audio features in building well-performing classifiers. In this paper, we …

Traditionally, speech emotion recognition (SER) research has relied on manually
handcrafted acoustic features using feature engineering. However, the design of …

We present M3ER, a learning-based method for emotion recognition from multiple input
modalities. Our approach combines cues from multiple co-occurring modalities (such as …

Speech emotion recognition (SER) is an active research field of digital signal processing
and plays a crucial role in numerous applications of Human–computer interaction (HCI) …

Emotion recognition from speech is a challenging task. Recent advances in deep learning
have led bi-directional recurrent neural network (Bi-RNN) and attention mechanism as a …