Speech emotion recognition (SER) is traditionally performed using merely acoustic
information. Acoustic features, commonly are extracted per frame, are mapped into emotion …

Deep Learning has implemented a wide range of applications and has become increasingly
popular in recent years. The goal of multimodal deep learning (MMDL) is to create models …

The RAVDESS is a validated multimodal database of emotional speech and song. The
database is gender balanced consisting of 24 professional actors, vocalizing lexically …

In this paper, we first provide a review of the state-of-the-art emotional voice conversion
research, and the existing emotional speech databases. We then motivate the development …

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

Multimodal fusion has been proved to improve emotion recognition performance in previous
works. However, in real-world applications, we often encounter the problem of missing …

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 …

Speech emotion recognition is one of the challenging research issues in the knowledge-
based system and various methods have been recommended to reach high classification …

Recognizing emotions during social interactions has many potential applications with the
popularization of low-cost mobile sensors, but a challenge remains with the lack of …

In recent years, affective computing has become a topic of considerable interest, driven by
its ability to enhance several domains, such as mental health monitoring, human–computer …