Since emotional speech can be regarded as a variation on neutral (non-emotional) speech, it is expected that a robust neutral speech model can be useful in contrasting different emotions expressed in speech. This study explores this idea by creating acoustic models trained with spectral features, using the emotionally-neutral TIMIT corpus. The performance is tested with two emotional speech dat...

This paper proposes a hybrid approach based on neuro fuzzy model and emotional learning for prediction of stock exchange market. Neuro fuzzy models are powerful in modeling and forecasting highly nonlinear and complex time series. The emotional Learning, which is successfully used in bounded rational decision making, is introduced as an appropriate method to achieve particular goals in the pred...

Understanding the emotional appeal of paintings is a significant research problem related to a↵ective image classification. The problem is challenging in part due to the scarceness of manually-classified paintings. Our work proposes to apply statistical models trained over photographs to infer the emotional appeal of paintings. Directly applying the learned models on photographs to paintings ca...

Affect recognition is a crucial requirement for future human machine interfaces to effectively respond to nonverbal behaviors of the user. Speech emotion recognition systems analyze acoustic features to deduce the speaker’s emotional state. However, human voice conveys a mixture of information including speaker, lexical, cultural, physiological and emotional traits. The presence of these commun...

In the field of design, it is accepted that users’ perceptions of systems are influenced by emotion as much as cognition, and functionally-complete products will not be adopted if they do not appeal to emotions. While software engineering methodologies have matured to handle non-functional requirements such as usability, what has not been investigated fully is the emotional needs of people. Tha...

Emotion perception is a complex process, often measured using stimuli presentation experiments that query evaluators for their perceptual ratings of emotional cues. These evaluations contain large amounts of variability both related and unrelated to the evaluated utterances. One approach to handling this variability is to model emotion perception at the individual level. However, the perception...

Using outpatients with anxiety and mood disorders (N = 350), the authors tested several models of the structural relationships of dimensions of key features of selected emotional disorders and dimensions of the tripartite model of anxiety and depression. Results supported the discriminant validity of the 5 symptom domains examined (mood disorders: generalized anxiety disorder, GAD; panic disord...

Recent work on emotional speech processing has demonstrated the interest to consider the information conveyed by the emotional component in speech to enhance the understanding of human behaviors. But to date, there has been little integration of emotion detection systems in effective applications. The present research focuses on the development of a fear-type emotions recognition system to dete...

Contemporary cognitive science attempts to provide computational models that describe how consciousness and emotion constitute adaptive behavior. Given the recent neurobiological view that highlights the fact that the cognitive and emotional regions of the brain work together to achieve conscious behavior, it was shown that signal-detection theory (SDT) can effectively capture the notion of the...

Speech signal in addition to the linguistic information contains additional information about the speaker: age, gender, social status, accent (foreign accent, dialects, etc.), emotional state, health etc. Some of these informational channels induce changes of the speech acoustic characteristics. This article presents evaluation of the ASR acoustic models (first trained on neutral, read speech) ...