Customer experience (CX) has emerged as a sustainable source of competitive
differentiation. Recent developments in big data analytics (BDA) have exposed possibilities …

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 (SER) classifies speech into emotion categories such as:
Happy, Angry, Sad and Neutral. Recently, deep learning has been applied to the SER task …

Multimodal emotion recognition from speech is an important area in affective computing.
Fusing multiple data modalities and learning representations with limited amounts of labeled …

Emotion Recognition is a challenging research area given its complex nature, and humans
express emotional cues across various modalities such as language, facial expressions …

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

The expression of emotions in human communication plays a very important role in the
information that needs to be conveyed to the partner. The forms of expression of human …

The traditional approach of single-word brand names faces constraints due to trademarks,
prompting a shift towards fusing two or more words to craft unique and memorable brands …

Significant research has been conducted during the last decade on the application of
machine learning for speech processing, particularly speech recognition. However, in recent …

When a multi-modal affective analysis model generalizes from a bimodal task to a trimodal
or multi-modal task, it is usually transformed into a hierarchical fusion model based on every …