In this paper we present a method to automatically identify linguistic contexts which contain possible causes of emotions or emotional states from Italian newspaper articles (La Repubblica Corpus). Our methodology is based on the interplay between relevant linguistic patterns and an incremental repository of common sense knowledge on emotional states and emotion eliciting situations. Our approa...

We propose a speaker emotional state classification method that employs inference-based Bayesian networks to learn posterior density of emotional speech sequentially. We aim to alleviate difficulty in detecting medium-term states where the required monitoring time is longer compared to shortterm emotional states that makes temporal content representation harder. Our inference algorithm takes ad...

Participants in manipulated emotional states played computerised movies in which facial expressions of emotion changed into categorically different expressions. The participants’ task was to detect the offset of the initial expression. An effect of emotional state was observed such that individuals in happy states saw the offset of happiness (changing into sadness) at an earlier point in the mo...

Emotion recognition is an important factor of affective computing and has potential use in assistive technologies. In this paper we used landmark and other acoustic features to recognize different emotional states in speech. We analyzed 2442 utterances from the Emotional Prosody Speech and Transcripts corpus and extracted 62 features from each utterance. A neural network classifier was built to...

This paper studies the influence of learner’s affective states on the well established EEG-mental engagement index during a problem solving task. The electrical activity of the human brain, known as electroencephalography or EEG was registered according to an acquisition protocol in a learning environment specifically constructed for emotional elicitation. Data were gathered from 35 healthy sub...

This paper proposes an emotion recognition system using EEG signals and higher order spectra. A visual induction based acquisition protocol is designed for recording the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) under two emotional states of participants, calm-neutral and negatively exited. After pre-processing the signals, higher order spectra are employed to extract the features ...

Design team interactions are one of the least understood aspects of the engineering design process. Given the integral role that designers play in the engineering design process, understanding the emotional states of individual design team members will help us quantify interpersonal interactions and how those interactions affect resulting design solutions. The methodology presented in this pape...

Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emo...

This paper presents a novel application of speech emotion recognition: estimation of the level of conversational engagement between users of a voice communication system. We begin by using machine learning techniques, such as the support vector machine (SVM), to classify users’ emotions as expressed in individual utterances. However, this alone fails to model the temporal and interactive aspect...

This paper presents an approach to automatic recognition of emotional states from audio-visual bimodal signals using semi-coupled hidden Markov model and error weighted classifier combination for Human-Computer Interaction (HCI). The proposed model combines a simplified state-based bimodal alignment strategy and a Bayesian classifier weighting scheme to obtain the optimal solution for audio-vis...