Speech is the most natural way of expressing ourselves as humans. It is only natural then to
extend this communication medium to computer applications. We define speech emotion …

Generative models have been in existence for many decades. In the field of machine
learning, we come across many scenarios when directly learning a target is intractable …

The advancements in neural networks and the on-demand need for accurate and near real-
time Speech Emotion Recognition (SER) in human–computer interactions make it …

Emotions are an integral part of human interactions and are significant factors in determining
user satisfaction or customer opinion. speech emotion recognition (SER) modules also play …

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

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) …

Speech technology is not appropriately explored even though modern advances in speech
technology—especially those driven by deep learning (DL) technology—offer …

The ultimate goal of transfer learning is to reduce labeled data requirements by exploiting a
pre-existing embedding model trained for different datasets or tasks. The visual and …

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

We propose a novel training strategy for Tacotron-based text-to-speech (TTS) system that
improves the speech styling at utterance level. One of the key challenges in prosody …