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1

Enhancing driver safety with ECG-based emotion recognition using BiLSTM networks

Chandra Raga Madhuri, Vanapalli Satya Sumanth, Neelaiahgari Giri Venkata Sai Tej

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2026

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T. 16, nr 1

21--28

EN

Emotions critically influence human decision making and behaviour, particularly in safety-sensitive contexts like driving. This study introduces an ECG-based emotion recognition framework suitable for online driver monitoring system that exclusively analyses electrocardiogram (ECG) signals through a Bidirectional Long Short-Term Memory (BiLSTM) network. The framework captures temporal dynamics in physiological features – including heart rate variability and signal entropy – to classify seven emotional states (neutral, happy, sad, angry, fear, surprise, disgust) with high accuracy. Beyond detection, the system incorporates an intelligent recommendation mechanism designed to mitigate emotional distractions demonstrating how emotion predictions could be translated into driver-support feedback. Experimental validation on synthetic ECG data demonstrates robust emotion classification performance in identifying complex emotional patterns from ECG data, outperforming conventional unimodal approaches. By bridging affective computing with intelligent transportation systems, this work advances the development of adaptive Driver Assistance Systems (DAS) that prioritize both road safety and user wellbeing. The proposed system’s real-time capability and nonintrusive design position it as a scalable solution for emotion aware environments, demonstrating the potential of ECG-based emotion recognition as a supporting component for future driver assistance systems. This research contributes to the growing field of affective human-machine interaction while demonstrating practical applications for intelligent transport systems.

PL

Emocje mają decydujący wpływ na procesy decyzyjne i zachowania człowieka, zwłaszcza w sytuacjach wymagających szczególnej ostrożności, takich jak prowadzenie pojazdu. W niniejszym badaniu przedstawiono system rozpoznawania emocji oparty na EKG, przeznaczony do monitorowania kierowców w czasie rzeczywistym, który analizuje wyłącznie sygnały elektrokardiograficzne (EKG) za pomocą sieci dwukierunkowej pamięci długo- i krótkoterminowej (BiLSTM). System ten rejestruje dynamikę czasową cech fizjologicznych – w tym zmienność rytmu serca i entropię sygnału – w celu klasyfikacji siedmiu stanów emocjonalnych (neutralny, radosny, smutny, zły, strach, zaskoczenie, obrzydzenie) z dużą dokładnością. Oprócz wykrywania, system zawiera inteligentny mechanizm rekomendacji zaprojektowany w celu łagodzenia emocjonalnych czynników rozpraszających uwagę, pokazując, w jaki sposób prognozy emocjonalne mogą zostać przełożone na informacje zwrotne wspierające kierowcę. Weryfikacja eksperymentalna na syntetycznych danych EKG wykazuje solidną wydajność klasyfikacji emocji w identyfikowaniu złożonych wzorców emocjonalnych na podstawie danych EKG, przewyższając konwencjonalne podejścia jednomodalne. Łącząc informatykę afektywną z inteligentnymi systemami transportowymi, niniejsza praca przyczynia się do rozwoju adaptacyjnych systemów wspomagania kierowcy (DAS), które stawiają na pierwszym miejscu zarówno bezpieczeństwo na drogach, jak i dobre samopoczucie użytkowników. Dzięki możliwościom działania w czasie rzeczywistym oraz nieinwazyjnej konstrukcji proponowany system stanowi skalowalne rozwiązanie dla środowisk rozpoznających emocje, wykazując potencjał rozpoznawania emocji na podstawie EKG jako elementu wspierającego przyszłe systemy wspomagania kierowcy. Badania te wnoszą wkład w rozwijającą się dziedzinę afektywnej interakcji człowiek-maszyna, jednocześnie pokazując praktyczne zastosowania w inteligentnych systemach transportowych.

2

A dynamic spectrum driven network for enhanced multimodal emotion recognition with EEG and ECG signals

Samal Priyadarsini, Hashmi Mohammad Farukh

Biocybernetics and Biomedical Engineering

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2026

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Vol. 46, iss. 1

139--161

EN

For effective human-machine interaction, utilizing various physiological cues to recognize emotions is crucial. Using many physiological signals yields more accurate outcomes when recognizing human emotional states. This study introduces a new approach called DSDNet (Dynamic spectrum driven network) to emotion recognition using Electroencephalogram (EEG) and Electrocardiogram (ECG) signals. The method involves a dynamic time frequency analysis technique that combines synchrosqueezed transform with short time fast fractional Fourier transform. The signals are divided into segments, and the corresponding time-frequency spectrograms from EEG and ECG signals are combined for additional assessment and the importance of these spectrogram features are visualized by using SHAP deep explainer. Subsequently, these spectrogram features are provided to a simple efficient convolutional neural network for classification. The proposed approach utilized the DREAMER and AMIGOS datasets for development and comparison with several high-performance algorithms. This approach surpassed the most notable results in the existing literature, with an accuracy of 98.6%, 98.9%, and 99.2% for the valence, arousal, and dominance categories respectively, when applied to the DREAMER dataset. Similarly, when applied to the AMIGOS dataset, it achieved accuracies of 98.8%, 99.5%, and 99.4% for all three categories. Therefore, the findings of this research indicate that by incorporating various physiological signals and modern approaches in the field of human-machine interaction, it is possible to greatly enhance the accuracy of emotion detection results.

3

Recognizing user emotion based on keystroke dynamics

Malinowski Michał, Krawczyk-Borysiak Zuzanna

Przegląd Elektrotechniczny

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2024

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R. 100, nr 6

19--22

EN

The paper presents a study concerning recognizing user emotion based on keystroke dynamics of the written text. At first, the analysis of the dataset used in the task is performed. Followed by the training and the effectiveness assessment of classical methods: Naive Bayes, K-Nearest Neighbours, Random Forest, and Multilayer Perceptron applied to the classification of provided samples to one of four emotions: anger, calm, happiness, sadness. The precision, recall, F1-score and time performance are evaluated. The Random Forest and MLP classifiers performed best, with an overall F1 measure of 84.83% and 80.47%, respectively. The scenario for extending the data set is proposed, along with the analysis of classification results of new data.

PL

Artykuł przedstawia badania dotyczące rozpoznawania emocji użytkownika na podstawie dynamiki naciśnięć klawiszy wpisywanego tekstu. W pracy przeprowadzono analizę wykorzystywanego zbioru danych, wytrenowano oraz dokonano oceny skuteczności klasycznych metod takich jak: naiwny klasyfikator Bayesa, metoda najbliższych sąsiadów, las losowy oraz perceptron wielowarstwowy, zastosowanych do przyporządkowania danych do jednej z czterech emocji: złości, spokoju, radości lub smutku. Uzyskane wyniki zostały ewaluowane z wykorzystaniem miar precyzji, czułości oraz F1, oceniono również wydajność czasową. Las losowy oraz perceptron wielowarstwowy osiągnęły najlepsze wyniki, z wynikiem F1 równym odpowiednio 84.83% i 80.47%. Zaprezentowano również scenariusz rozszerzenia zbioru danych, razem z analizą wyników klasyfikacji nowych danych.

4

EEG based emotion analysis using reinforced spatio-temporal attentive graph neural and contextnet techniques

Devi C. Akalya, Renuka D. Karthika

Journal of Automation Mobile Robotics and Intelligent Systems

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2024

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Vol. 18, No. 3

61--68

EN

EEG-based emotion classification is considered to separate and observe the mental state or emotions. Emotion classification using EEG is used for medical, security and other purposes. Several deep learning and machine learning strategies are employed to classify the EEG emotion signals. They do not provide sufficient accuracy and have higher complexity and high error rate. In this manuscript, a novel Reinforced Spatio-Temporal Attentive Graph Neural Networks (RSTAGNN) and ContextNet for emotion classification with EEG signals is proposed (RSTAGNN-ContextNet-GWOA-EEG-EA). Here, the input EEG signals are taken from two benchmark datasets,namely DEAP and K-EmoCon datasets. Then, the input EEG signals are pre-processed,and the fea- tures are extracted utilizing ContextNet with Global Principal Component Analysis (GPCA). After that, the EEG signal emotions are classified using Reinforced Spatio- Temporal Attentive Graph Neural Networks method. RSTAGNN weight parameters are optimized under the Glowworm Swarm Optimization Algorithm (GWOA). The proposed model classifies the EEG signal emotions with high accuracy. The efficacy of the proposed method using the DEAP dataset attains higher accuracy by 24.05%, 12.64% related to existing systems, like Multi-domain feature fusion for emotion classification (DWT-SVM-EEG- EA-DEAP), EEG emotion finding utilizing fusion mode of graph CNN with LSTM (GCNN-LSTM-EEG-EA-DEAP) respectively. The efficiency of the proposed method using the K-EmoCon dataset attains higher accuracy 32.64%, 15.65% related to existing systems, like Toward Robust Wearable Emotion Realization along Contrastive Repre- sentation Learning (CAT-EEG-EA-K-EmoCon) and Human Emotion Recognition using Physiological Signals (CAT- EEG-EA-K-EmoCon) respectively.

5

Interpreting convolutional layers in dnn model based on time : frequency representation of emotional speech

Smietanka Lukasz, Maka Tomasz

Journal of Artificial Intelligence and Soft Computing Research

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2024

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Vol. 14, No. 1

5--23

EN

The paper describes the relations of speech signal representation in the layers of the convolutional neural network. Using activation maps determined by the Grad-CAM algorithm, energy distribution in the time–frequency space and their relationship with prosodic properties of the considered emotional utterances have been analysed. After preliminary experiments with the expressive speech classification task, we have selected the CQT-96 time–frequency representation. Also, we have used a custom CNN architecture with three convolutional layers in the main experimental phase of the study. Based on the performed analysis, we show the relationship between activation levels and changes in the voiced parts of the fundamental frequency trajectories. As a result, the relationships between the individual activation maps, energy distribution, and fundamental frequency trajectories for six emotional states were described. The results show that the convolutional neural network in the learning process uses similar fragments from time–frequency representation, which are also related to the prosodic properties of emotional speech utterances. We also analysed the relations of the obtained activation maps with time-domain envelopes. It allowed observing the importance of the speech signals energy in classifying individual emotional states. Finally, we compared the energy distribution of the CQT representation in relation to the regions’ energy overlapping with masks of individual emotional states. In the result, we obtained information on the variability of energy distributions in the selected signal representation speech for particular emotions.

6

Bridging the gap between AI and human emotion: a multimodal recognition system

Neeraja Ganta, Teja Jakkula Sai Surya, Ravi Kumar M., Prasanna Lakshmi J., Parvez Muzammil M., Santhosh Chella

Control and Cybernetics

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2024

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Vol. 53, No. 4

619--638

EN

This study introduces a novel system that integrates voice and facial recognition technologies to enhance human-computer interaction by accurately interpreting and responding to user emotions. Unlike conventional approaches that analyze either voice or facial expressions in isolation, this system combines both modalities, offering a more comprehensive understanding of emotional states. By evaluating facial expressions, vocal tones, and contextual conversation history, the system generates personalized, context-aware responses, fostering more natural and empathetic AI interactions. This advancement significantly improves user engagement and satisfaction, paving the way for emotionally intelligent AI applications across diverse fields.

7

Social robot response to negative emotions as a PDDL planning problem in the presence of uncertainty

Probierz Eryka, Gałuszka Anita, Gałuszka Adam

Przegląd Elektrotechniczny

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2023

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R. 99, nr 8

33--37

EN

We propose to use automatic scheduling in the presence of uncertainty methodology to analyze the emotional state of a person and possible responses of a social robot. The emotions considered were: Sadness, Fear, Anger, Disgust and Contempt. The scenarios considered include modelling uncertainty in emotion detection. The result of the work is a set of two planning domains with illustrative examples. It was assumed that when negative emotions are detected, the robot should react in such a way as to reduce or not escalate them.

PL

Proponujemy wykorzystanie metodologii automatycznego planowania w obecności niepewności do analizy stanu emocjonalnego osoby i możliwych reakcji robota społecznego. Rozważane emocje to: Smutek, Strach, Złość, Obrzydzenie i Pogarda. Rozważane scenariusze obejmują modelowanie niepewności w detekcji emocji. Efektem pracy jest zestaw dwóch domen planistycznych wraz z ilustrującymi je przykładami. Założono, że w przypadku wykrycia negatywnych emocji robot powinien reagować w taki sposób, aby je zmniejszyć lub nie eskalować.

8

Gender-aware speaker's emotion recognition based on 1-D and 2-D features

Kasprzak Włodzimierz, Hryciów Mateusz

Annals of Computer Science and Information Systems

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2023

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Vol. 35

1011--1015

EN

An approach to speaker's emotion recognition based on several acoustic feature types and 1D convolutional neural networks is described. The focus is on selecting the best speech features, improving the baseline model configuration and integrating in the solution a gender classification network. Features include a Mel-scale spectrogram and MFCC- , Chroma-, prosodic- and pitch-related features. Especially, the question whether to use 2-D maps of features or reduce them to 1-D vectors by averaging, is experimentally resolved. Well--known speech datasets RAVDESS, Tess, Crema-D and Savee are used in experiments. It appeared, that the best performing model consists of two convolutional networks for gender-aware classification and one gender classifier. The Chroma features have been found to be obsolete, and even disturbing, given other speech features. The f1 accuracy of proposed solution reached 73.2% on the RAVDESS dataset and 66.5% on all four datasets combined, improving the baseline model by 7.8% and 3%, respectively. This approach is an alternative to other proposed models, which reported accuracy scores of 60% - 71% on the RAVDESS dataset.

9

Multi-model hybrid ensemble weighted adaptive approach with decision level fusion for personalized affect recognition based on visual cues

Jadhav Nagesh, Sugandhi Rekha

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 6

art. no. e138819

EN

In the domain of affective computing different emotional expressions play an important role. To convey the emotional state of human emotions, facial expressions or visual cues are used as an important and primary cue. The facial expressions convey humans affective state more convincingly than any other cues. With the advancement in the deep learning techniques, the convolutional neural network (CNN) can be used to automatically extract the features from the visual cues; however variable sized and biased datasets are a vital challenge to be dealt with as far as implementation of deep models is concerned. Also, the dataset used for training the model plays a significant role in the retrieved results. In this paper, we have proposed a multi-model hybrid ensemble weighted adaptive approach with decision level fusion for personalized affect recognition based on the visual cues. We have used a CNN and pre-trained ResNet-50 model for the transfer learning. VGGFace model’s weights are used to initialize weights of ResNet50 for fine-tuning the model. The proposed system shows significant improvement in test accuracy in affective state recognition compared to the singleton CNN model developed from scratch or transfer learned model. The proposed methodology is validated on The Karolinska Directed Emotional Faces (KDEF) dataset with 77.85% accuracy. The obtained results are promising compared to the existing state of the art methods.

10

Using BCI and EEG to process and analyze driver’s brain activity signals during VR simulation

Nader Mirosław, Jacyna-Gołda Ilona, Nader Stanisław, Nehring Karol

Archives of Transport

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2021

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Vol. 60, iss. 4

137--153

EN

The use of popular brain–computer interfaces (BCI) to analyze signals and the behavior of brain activity is a very current problem that is often undertaken in various aspects by many researchers. This comparison turns out to be particularly useful when studying the flows of information and signals in the human-machine-environment system, especially in the field of transportation sciences. This article presents the results of a pilot study of driver behavior with the use of a pro-prietary simulator based on Virtual Reality technology. The study uses the technology of studying signals emitted by the human mind and its specific zones in response to given environmental factors. A solution based on virtual reality with the limitation of external stimuli emitted by the real world was proposed, and computational analysis of the obtained data was performed. The research focused on traffic situations and how they affect the subject. The test was attended by representatives of various age groups, both with and without a driving license. This study presents an original functional model of a research stand in VR technology that we designed and built. Testing in VR conditions allows to limit the influence of undesirable external stimuli that may distort the results of readings. At the same time, it increases the range of road events that can be simulated without generating any risk for the participant. In the presented studies, the BCI was used to assess the driver's behavior, which allows for the activity of selected brain waves of the examined person to be registered. Electro-encephalogram (EEG) was used to study the activity of brain and its response to stimuli coming from the Virtual Reality created environment. Electrical activity detection is possible thanks to the use of electrodes placed on the skin in selected areas of the skull. The structure of the proprietary test-stand for signal and information flow simulation tests, which allows for the selection of measured signals and the method of parameter recording, is presented. An important part of this study is the presentation of the results of pilot studies obtained in the course of real research on the behavior of a car driver.

11

Acoustic Methods in Identifying Symptoms of Emotional States

Piątek Zuzanna, Kłaczyński Maciej

Archives of Acoustics

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2021

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Vol. 46, No. 2

259--269

EN

The study investigates the use of speech signal to recognise speakers’ emotional states. The introduction includes the definition and categorization of emotions, including facial expressions, speech and physiological signals. For the purpose of this work, a proprietary resource of emotionally-marked speech recordings was created. The collected recordings come from the media, including live journalistic broadcasts, which show spontaneous emotional reactions to real-time stimuli. For the purpose of signal speech analysis, a specific script was written in Python. Its algorithm includes the parameterization of speech recordings and determination of features correlated with emotional content in speech. After the parametrization process, data clustering was performed to allows for the grouping of feature vectors for speakers into greater collections which imitate specific emotional states. Using the t-Student test for dependent samples, some descriptors were distinguished, which identified significant differences in the values of features between emotional states. Some potential applications for this research were proposed, as well as other development directions for future studies of the topic.

12

The Virtual Emotion Loop: Towards Emotion-Driven Product Design via Virtual Reality

Andreoletti Davide, Luceri Luca, Peternier Achille, Leidi Tiziano, Giordano Silvia

Annals of Computer Science and Information Systems

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2021

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Vol. 25

371--378

EN

Emotions play a significant role in product design for end-users. However, how to take emotions into account is not yet completely understood. We argue that this gap is due to a lack of methodological and technological frameworks for effective investigation of the elicitation conditions related to emotions and corresponding emotional responses of the users. Emotion-driven design should encompass a thorough assessment of users' emotional reactions in relation to certain elicitation conditions. By using Virtual Reality (VR) as mean to perform this investigation, we propose a novel methodological framework, referred to as the VR-Based Emotion-Elicitation-and-Recognition loop (VEE-loop), to close this gap.

13

Design and application of facial expression analysis system in empathy ability of children with autism spectrum disorder

Guo Chen, Zhang Kun, Chen Jingying, Xu Ruyi, Gao Lei

Annals of Computer Science and Information Systems

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2021

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Vol. 25

319--325

EN

Empathy is an important social ability in early childhood development. One of the significant characteristics of children with autism spectrum disorder (ASD) is their lack of empathy, which makes it difficult for them to understand other's emotions and to judge other's behavioral intentions, leading to social disorders. This research designed and implemented a facial expression analysis system that could obtain and analyze the real-time expressions of children when viewing stimulus, and evaluate the empathy differences between ASD children and typical development children. The research results provided new ideas for evaluation of ASD children, and helped to develop empathy intervention plans.

14

Comparison of different feature extraction methods for EEG-based emotion recognition

Nawaz Rab, Cheah Kit Hwa, Nisar Humaira, Yap Vooi Voon

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

910--926

EN

EEG-based emotion recognition is a challenging and active research area in affective computing. We used three-dimensional (arousal, valence and dominance) model of emotion to recognize the emotions induced by music videos. The participants watched a video (1 min long) while their EEG was recorded. The main objective of the study is to identify the features that can best discriminate the emotions. Power, entropy, fractal dimension, statistical features and wavelet energy are extracted from the EEG signals. The effects of these features are investigated and the best features are identified. The performance of the two feature selection methods, Relief based algorithm and principle component analysis (PCA), is compared. PCA is adopted because of its improved performance and the efficacies of the features are validated using support vector machine, K-nearest neighbors and decision tree classifiers. Our system achieves an overall best classification accuracy of 77.62%, 78.96% and 77.60% for valence, arousal and dominance respectively. Our results demonstrated that time-domain statistical characteristics of EEG signals can efficiently discriminate different emotional states. Also, the use of three-dimensional emotion model is able to classify similar emotions that were not correctly classified by two-dimensional model (e.g. anger and fear). The results of this study can be used to support the development of real-time EEG-based emotion recognition systems.

15

Analysis of Features and Classifiers in Emotion Recognition Systems : Case Study of Slavic Languages

Nedeljković Željko, Milošević Milana, Ðurović Željko

Archives of Acoustics

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2020

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Vol. 45, No. 1

129--140

EN

Today’s human-computer interaction systems have a broad variety of applications in which automatic human emotion recognition is of great interest. Literature contains many different, more or less successful forms of these systems. This work emerged as an attempt to clarify which speech features are the most informative, which classification structure is the most convenient for this type of tasks, and the degree to which the results are influenced by database size, quality and cultural characteristic of a language. The research is presented as the case study on Slavic languages.

16

Classifying and Visualizing Emotions with Emotional DAN

Tautkutė Ivona, Trzciński Tomasz

Fundamenta Informaticae

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2019

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Vol. 168, nr 2-4

269--285

EN

Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multi-layered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (DAN) with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%. Furthermore, we visualize image regions analyzed by the network when making a decision and the results indicate that our EmotionalDAN model is able to correctly identify facial landmarks responsible for expressing the emotions.

17

Speech Emotion Recognition Based on Voice Fundamental Frequency

Dimitrova-Grekow Teodora, Klis Aneta, Igras-Cybulska Magdalena

Archives of Acoustics

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2019

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Vol. 44, No. 2

277--286

EN

The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.

18

Machine-learning at the service of plastic surgery: a case study evaluating facial attractiveness and emotions using R language

Štěpánek Lubomír, Měšťák Jan, Kasal Pavel

Annals of Computer Science and Information Systems

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2019

|

Vol. 18

107--112

EN

Since the plastic surgery should consider that facial impression is always dependent on current facial emotion, it came to be verified how precise classification of facial images into sets of defined facial emotions is.

19

Modeling the 2D space of emotions based on the poincare plot of heart rate variability signal

Moharreri S., Dabanloo N. J., Maghooli K.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 4

794--809

EN

Emotions mean accepting, understanding, and recognizing something with one's senses. The physiological signals generated from the internal organs of the body can objectively and realistically reflect changes in real-time human emotions and monitor the state of the body. In this study, the two-dimensional space-based emotion model was introduced on the basis of Poincare's two-dimensional plot of the signal of heart rate variability. Four main colors of psychology, blue, red, green, and yellow were used as a stimulant of emotion, and the ECG signals from 70 female students were recorded. Using extracted features of Poincare plot and heart rate asymmetry, two tree based models estimated the levels of arousal and valence with 0.05 mean square errors, determined an appropriate estimation of these two parameters of emotion. In the next stage of the study, four different emotions mean pleasure, anger, joy, and sadness, were classified using IF-THEN rules with the accuracy of 95.71%. The results show the color red is associated with more excitement and anger, while green has small anxiety. So, this system provides a measure for numerical comparison of mental states and makes it possible to model emotions for interacting with the computer and control mental states independently of the pharmaceutical methods.

20

The Acoustic Cues of Fear : Investigation of Acoustic Parameters of Speech Containing Fear

Özseven T.

Archives of Acoustics

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2018

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Vol. 43, No. 2

245--251

EN

Speech emotion recognition is an important part of human-machine interaction studies. The acoustic analysis method is used for emotion recognition through speech. An emotion does not cause changes on all acoustic parameters. Rather, the acoustic parameters affected by emotion vary depending on the emotion type. In this context, the emotion-based variability of acoustic parameters is still a current field of study. The purpose of this study is to investigate the acoustic parameters that fear affects and the extent of their influence. For this purpose, various acoustic parameters were obtained from speech records containing fear and neutral emotions. The change according to the emotional states of these parameters was analyzed using statistical methods, and the parameters and the degree of influence that the fear emotion affected were determined. According to the results obtained, the majority of acoustic parameters that fear affects vary according to the used data. However, it has been demonstrated that formant frequencies, mel-frequency cepstral coefficients, and jitter parameters can define the fear emotion independent of the data used.