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 …

Automatic emotion recognition from speech is a challenging task which relies heavily on the
effectiveness of the speech features used for classification. In this work, we study the use of …

The lack of a large, natural emotional database is one of the key barriers to translate results
on speech emotion recognition in controlled conditions into real-life applications. Collecting …

We present the MSP-IMPROV corpus, a multimodal emotional database, where the goal is
to have control over lexical content and emotion while also promoting naturalness in the …

In this paper, we propose a novel domain invariant feature learning (DIFL) method to deal
with speaker-independent speech emotion recognition (SER). The basic idea of DIFL is to …

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 …

In this paper, we are interested in exploiting textual and acoustic data of an utterance for the
speech emotion classification task. The baseline approach models the information from …

Emotions have a crucial function in the mental existence of humans. They are vital for
identifying a person's behaviour and mental condition. Speech Emotion Recognition (SER) …

Computational representation of everyday emotional states is a challenging task and,
arguably, one of the most fundamental for affective computing. Standard practice in emotion …

Multi-modal speech emotion recognition is a study to predict emotion categories by
combining speech data with other types of data, such as video, speech text transcription …