Research on speech processing has traditionally considered the task of designing hand-
engineered acoustic features (feature engineering) as a separate distinct problem from the …

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

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 …

The performance of speech emotion recognition is affected by the differences in data
distributions between train (source domain) and test (target domain) sets used to build and …

Conversational emotion recognition is a crucial research topic in human-computer
interactions. Due to the heavy annotation cost and inevitable label ambiguity, collecting …

In this paper we present our findings on how representation learning on large unlabeled
speech corpora can be beneficially utilized for speech emotion recognition (SER). Prior work …

Recent advances in the field of machine learning have shown great potential for the
automatic recognition of apparent human emotions. In the era of Internet of Things and big …

Inspite the emerging importance of Speech Emotion Recognition (SER), the state-of-the-art
accuracy is quite low and needs improvement to make commercial applications of SER …

Speech emotion recognition (SER) systems find applications in various fields such as
healthcare, education, and security and defense. A major drawback of these systems is their …

An automatic speech recognition (ASR) system is a key component in current speech-based
systems. However, the surrounding acoustic noise can severely degrade the performance of …