Abstract Machine learning methods are extensively used for processing and analysing
speech signals by virtue of their performance gains over multiple domains. Deep learning …

The speech emotion recognition (SER) field has been active since it became a crucial
feature in advanced Human-Computer Interaction (HCI), and wide real-life applications use …

Methods for detecting emotions that employ many modalities at the same time have been
found to be more accurate and resilient than those that rely on a single sense. This is due to …

Multimodal human–computer interaction (HCI) systems pledge a more human–human-like
interaction between machines and humans. Their prowess in emanating an unambiguous …

Most speech emotion recognition methods based on frames have obtained good results in
many applications. However, they segment each speech sample into smaller frames that are …

Speech emotion recognition is a substantial component of natural language processing
(NLP). It has strict requirements for the effectiveness of feature extraction and that of the …

Affective Computing systems can detect the emotional state and mindset of an individual.
Speech Emotion Recognition (SER) is a unimodal affect computing system based on …

Speech emotion recognition (SER) is a non-trivial task for humans, while it remains
challenging for automatic SER due to the linguistic complexity and contextual distortion …

Emotions can be expressed through multiple complementary modalities. This study selected
speech and facial expressions as modalities by which to recognize emotions. Current …

Speech emotion recognition (SER) plays a pivotal role in enhancing Human-computer
interaction (HCI) systems. This paper introduces a groundbreaking Capsule-enhanced …