Speech emotion recognition is currently an active research subject and has attracted extensive interest in the science community due to its vital application to human‐robot interaction. Most speech emotion recognition systems employ high‐dimensional speech features, indicating human emotion expression, to improve emotion recognition performance. To effectively reduce t...

Along with automatic speech recognition, many researchers have been actively studying emotion since information is as crucial the textual for effective interactions. Emotion can be divided into categorical and dimensional emotion. Although widely used, emotion, typically represented arousal valence, provide more detailed on emotional states. Therefore, in this paper, we propose a Conformer-base...

Dyadic interactions encapsulate rich emotional exchange between interlocutors suggesting a multimodal, cross-speaker and cross-dimensional continuous emotion dependency. This study explores the dynamic inter-attribute emotional dependency at the cross-subject level with implications to continuous emotion recognition based on speech and body motion cues. We propose a novel two-stage Gaussian Mix...

Music is widely perceived as expressive of emotion. Music plays such a fundamental role in society economically, culturally and in people’s personal lives that the emotional impact of music on people is extremely relevant. Research on automatic recognition of emotion in music usually approaches the problem from a classification perspective, comparing “emotional labels” calculated from different...

The empirical literature has presented inconsistent evidence for deficits in the recognition of basic emotion expressions in children with autism spectrum disorders (ASD), which may be due to the focus on research with relatively small sample sizes. Additionally, it is proposed that although children with ASD may correctly identify emotion expression they rely on more deliberate, more time-cons...

Speech and emotion recognition improve the quality of human computer interaction and allow easier to use interfaces for every level of user in software applications. In this study, we have developed two different neural networks called emotion recognition neural network (ERNN) and Gram-Charlier emotion recognition neural network (GERNN) to classify the voice signals for emotion recognition. The...

Facial emotion recognition impairments have been reported in Huntington's disease (HD). However, the nature of the impairments across the spectrum of HD remains unclear. We report on emotion recognition data from 344 participants comprising premanifest HD (PreHD) and early HD patients, and controls. In a test of recognition of facial emotions, we examined responses to six basic emotional expres...

In this paper, we proposed a new feature extraction method for emotion recognition based on the knowledge of the emotion production mechanism in physiology. It was reported by physiacoustist that emotional speech is differently encoded from the normal speech in terms of articulation organs and that emotion information in speech is concentrated in different frequencies caused by the different mo...

The ventromedial prefrontal cortex has been implicated in a variety of emotion processes. However, findings regarding the role of this region specifically in emotion recognition have been mixed. We used a sensitive facial emotion recognition task to compare the emotion recognition performance of 7 subjects with lesions confined to ventromedial prefrontal regions, 8 subjects with lesions elsewhe...

Most emotion recognition systems do not perform real-time emotion recognition due to latencies caused by phrase segmentation and resource-intensive feature acquisition, etc. To address this issue, we present an emotion recognition approach that can estimate speaker emotions with much lower latency. The proposed approach does not rely on phrase-level features to recognize speaker emotion; rather...