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Learning disability results from a reduced intellectual ability that can be observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Such condition may expose the children from the... more
Learning disability results from a reduced intellectual ability that can be observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Such condition may expose the children from the unfiltered porn contents freely available from the Internet as they are unaware or have minimal understanding of the negative effects of the pornographic contents. The exposure to pornographic contents that are unmonitored may result to porn addiction as it may trigger excitement and pleasure induced. Hence, this paper attempts to explore the empirical evidence of the correlation between learning disability and pornography addiction by using the electroencephalogram (EEG) of children from a private psychology clinic. The experimental results show that, there are weak correlation between learning disability based on the EEG frequency bands and porn addiction. It can be hoped that such approach is a stepping step in further exploring the relationship between porn addiction and learning disability.
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Culture refers to the cumulative knowledge, beliefs, values and concepts that are accepted by a group of people. Such information are shared and inherited from the previous generations in order for one to be blended and accepted in a... more
Culture refers to the cumulative knowledge, beliefs, values and concepts that are accepted by a group of people. Such information are shared and inherited from the previous generations in order for one to be blended and accepted in a society. Different cultural groups communicate differently that is distinct and unique making homogeneous interpretation of underlying emotional contents are more accurate. However, universality of cultural-influenced speech can be observed when cross cultural speeches are being interacted from different cultural groups to one another especially with the advancement of communication technology. In this study, two different cultural-influenced speech datasets representing American (NTU-American) and European (Netherland EmoSpeech) are employed to investigate their similarity and dissimilarity in term of heterogeneous listener's perception on the underlying emotional contents. The Mel Frequency Cepstral Coefficient (MFCC) feature extraction method and Multi Layer Perceptron (MLP) classifier are coupled to determine four different emotions, namely; anger, happiness, sadness and neutral acting as emotionless state. From the experimental result, it is noted that the proposed approach yielded accuracy performance of two times better than chance guessing. Moreover, the Netherland EmoSpeech dataset managed to obtain comparative accuracy with the established NTU-American dataset demonstrating that the data is satisfactory for speech emotion recognition purposes.
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There are many contributing factors that result in high number of traffic accidents on the roads and highways today. Globally, the human (operator) error is observed to be the leading cause. These errors may be transpired by the driver’s... more
There are many contributing factors that result in high number of traffic accidents on the roads and highways today. Globally, the human (operator) error is observed to be the leading cause. These errors may be transpired by the driver’s emotional state that leads to his/her uncontrolled driving behavior. It has been reported in a number of recent studies that emotion has direct influence on the driver behavior. In this chapter, the pre- and postaccident emotion of the driver is studied in order to better understand the behavior of the driver. A two-dimensional Affective Space Model (ASM) is used to determine the correlation between the driver behavior and the driver emotion. A 2-D ASM developed in this study consists of the valance and arousal values extracted from electroencephalogram (EEG) signals of ten subjects while driving a simulator under three different conditions consisting of initialization, pre-accident, and postaccident. The initialization condition refers to the subject’s brain signals during the initial period where he/she is asked to open and close his/her eyes. In order to elicit appropriate precursor emotion for the driver, the selected picture stimuli for three basic emotions, namely, happiness, fear, and sadness are used. The brain signals of the drivers are captured and labeled as the EEG reference signals for each driver. The Mel frequency cepstral coefficient (MFCC) feature extraction method is then employed to extract relevant features to be used by the multilayer perceptron (MLP) classifier to verify emotion. Experimental results show an acceptable accuracy for emotion verification and subject identification. Subsequently, a two-dimensional Affective Space Model (ASM) is employed to determine the correlation between the emotion and the behavior of drivers. The analysis using the 2-D ASM provides a visualization tool to facilitate better understanding of the pre- and postaccident driver emotion.
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AbstractIn this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. The brake and gas pedal pressure are used... more
AbstractIn this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. The brake and gas pedal pressure are used to identify uniqueness in driving maneuver o0f ...
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People typically associate health with only physical health. However, health is also interconnected to mental and emotional health. People who are emotionally healthy are in control of their behaviors and experience better quality of... more
People typically associate health with only physical health. However, health is also interconnected to mental and emotional health. People who are emotionally healthy are in control of their behaviors and experience better quality of life. Hence, understanding human behavior is very important in ensuring the complete understanding of one's holistic health. In this paper, we attempt to map human behavior state (HBS) profiles onto recalibrated speech affective space model (rSASM). Such an approach is derived from hypotheses that: 1) Behavior is influenced by emotion, 2) Emotion can be quantified through speech, 3) Emotion is dynamic and changes over time and 4) the emotion conveyance is conditioned by culture. Empirical results illustrated that the proposed approach can complement other types of behavior analysis in such a way that it offers more explanatory components from the perspective of emotion primitives (valence and arousal). Four different driving HBS; namely: distracted, laughing, sleepy and normal are profiled onto the rSASM to visualize the correlation between HBS and emotion. This approach can be incorporated in the future behavior analysis to envisage better performance.
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Humans sense, perceive, and convey emotion differently from each other due to physical, psychological, environmental, cultural, and language differences. For example, as recognized and studied by psychologists more than a century, it is... more
Humans sense, perceive, and convey emotion differently from each other due to physical, psychological, environmental, cultural, and language differences. For example, as recognized and studied by psychologists more than a century, it is easier for someone of the same culture to judge and recognize emotion correctly compared to those from different culture. In this chapter, we attempt to study the speech emotion recognition problem by using two speech corpora from the Berlin dataset and the NAW datasets. We have investigated the universality as well as diversity of two different cultural speech datasets recorded by German and American speakers, respectively. Experiments were conducted for identifying three basic emotions, namely, angry, sad, and happy with neutral as emotionless state from these datasets. MFCC coefficients were used as feature sets in the experiments, and MLP was employed as classifiers to compare the performance of these datasets. In addition, real-time recorded speech from drivers was also tested to see the performance in a vehicular setting. Finally, speech emotion profiling approach was introduced to explore the universality and diversity of the speech emotion features.
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People behave differently even under similar situations especially when driving.This is due to their behavior, exposure, experience, and judgment when facing certain phenomena, which in turn affect their driving capability. Each... more
People behave differently even under similar situations especially when driving.This is due to their behavior, exposure, experience, and judgment when facing certain phenomena, which in turn affect their driving capability. Each individual has a set of unique patterns of personality traits that derive and influence the behavior. With the advancement of technology, personality traits have become measurable. Therefore, driver behavior can be predicted to a certain degree through the assessment of driver personality. Personality is determined by means of interviews or self-reported questionnaires. However, these approaches are very much dependent on the truthfulness and honesty of the participants when answering the questionnaires, as they may have the tendency to exaggerate or suppress the answers. Hence, an alternative approach of using input without biasness of participants is needed. In this work, we employed neurophysiological input from brain signals captured from electroencephalograms (EEG) to measure emotion and link this to the understanding of personality. This is to study the correlation between the behavior and emotion based on the hypotheses that emotion influences on behavior and personality are affected by behavior. Experimental results indicate that emotion can be measured using the proposed approach, with accuracy ranging from 60% to 99% for happiness, fear, sadness, and calmness. The conscientiousness in personality traits is then measured and analyzed. It is found that there is a negative correlation between the conscientiousness and valence for fear, making it possible to detect this trait. These findings can be extended to understand driver behavior, which potentially could lead to safer driving and avoiding accidents
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Dyslexia is a language learning disability resulting in people experiencing difficulties in reading, spelling, writing and speaking due to inability in differentiating sound components. Detection of dyslexia for young children is a... more
Dyslexia is a language learning disability resulting in people experiencing difficulties in reading, spelling, writing and speaking due to inability in differentiating sound components. Detection of dyslexia for young children is a challenge because the signs and symptoms are not always obvious. It is also costly since experts are required to conduct and interpret the results of these evaluations. In this paper, the executive function tests were carried out on both control and dyslexic children and their respective brain waves are captured. By analyzing the electroencephalogram (EEG) patterns on the emotion dynamic while conducting the four executive functions, namely: identifying alphabet, recognizing colors, distinguishing between left and right and memory test, we are able to provide new insight into their emotional state. Kernel Density Estimation (KDE) feature extraction method coupled with Multi-layer Perceptron (MLP) are used to identify underlying different emotions during the executive function test. Experimental results indicated that there are substantial differences between dyslexic and control participants and the proposed method shows potential of extending such work for bigger scale analysis.
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Human speech communication will convey semantic information of the uttered word as well as the underlying emotion information of the interlocutor. Emotion identification is important, as it could enhance many applications added-features... more
Human speech communication will convey semantic information of the uttered word as well as the underlying emotion information of the interlocutor. Emotion identification is important, as it could enhance many applications added-features that can improve human computer interaction aspect. Such improvement surely can help to retain customer satisfaction and loyalty in the long run and serves as an attraction factor for a new customer. Although many researchers have used many approaches to recognize emotion from speech, no one can claim superiority of their findings. This is because different feature extraction methods coupled with various classifiers may produce different performance depending on the data used. This paper presents a comparative analysis of the speech emotion identification system using two different feature extraction methods of Mel Frequency Cepstral Coefficient (MFCC) and Linear Prediction Coefficient (LPC) coupled with Multilayer Perceptron (MLP) classifier. For further exploration, different numbers of MFCC filters are employed to observe the performance of the proposed system. The results indicate that MFCC-40 gives slightly better performance compared to the other MFCC coefficients in the Berlin EMO-DB and NTU_American whereas the MFCC-20 performs well for NTU_Asian. It is also observed that MFCC consistently performed better than LPC in all experiments, which are in-line with many reported findings. Such understanding can be extended to further study speech emotion in order to develop more robust and least error system in the future.
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Human recognizes speech emotions by extracting features from the speech signals received through the cochlea and later passed the information for processing. In this paper we propose the use of Mel-Frequency Cepstral Coefficient (MFCC) to... more
Human recognizes speech emotions by extracting features from the speech signals received through the cochlea and later passed the information for processing. In this paper we propose the use of Mel-Frequency Cepstral Coefficient (MFCC) to extract the speech emotion ...
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Research Interests: Psychology, Computer Science, Artificial Intelligence, Human Computer Interaction, Speech Recognition, and 10 moreSpeech, Emotion Recognition, Feature Extraction, Accuracy, Multilayer Perceptron, Mel Frequency Cepstral Coefficient, Perceptron, Artificial Neural Network, Fuzzy Neural Networks, and multi layer perceptron
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Customers' mood information is acquired to facilitate marketers' understanding in order to tailor the marketing strategies for positive outcomes optimization. Mood can be reasonably hypothesized as one of the factor that... more
Customers' mood information is acquired to facilitate marketers' understanding in order to tailor the marketing strategies for positive outcomes optimization. Mood can be reasonably hypothesized as one of the factor that influences customers' decision in buying the products or services offered. There have been many researchers reporting the correlation between moods and buying decision. However, to date, there is no such method that can exactlyquantify the customer's mood. Typically, a questionnaire is given to the participant to gauge their mood on the focused product or services. The drawback from such approach is that participants can fake, exaggerate or suppress their mood resulting to questionable inference. Hence, a new method of data acquisition is needed to be able to visualize the dynamics of the customers' mood for more accurate analysis. In this paper, the customer brain signal is captured using electroencephalogram (EEG) to track and record brain wave patterns in regard to their emotional states. The sequence of emotion is then used to identify their mood. A computational visualization technique is adopted to facilitate understanding of one minute emotion transition that assembling mood. The experimental results show that such approach is feasible and can be extended to study mood in a more comprehensive manner. It is envisaged that this work will be the catalyst for large mood data analysis tool that can help researchers in the near future to look at mood and buying decision for the improvement of comprehensive customer understanding in a more accurate manner.
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Abstract - Most speech emotion recognition system proposed to date uses the hidden Markov model (HMM). Such system is compute intensive and would require longer training and testing time that may not be suitable for on-line or smart phone... more
Abstract - Most speech emotion recognition system proposed to date uses the hidden Markov model (HMM). Such system is compute intensive and would require longer training and testing time that may not be suitable for on-line or smart phone application. In this paper we propose an alternative approach to realize the speech emotion recognition system by using Short Time Mel Frequency Cepstral Coefficient (ST-MFCC) as the features extraction method. The speaker independent emotion recognition system (SIERS) performance is measured based on three neural network and fuzzy neural network architecture; namely: Multi Layer Perceptron (MLP), Adaptive Neuro Fuzzy Inference System (ANFIS) and Generic Self-organising Fuzzy Neural Network (GenSoFNN). Experiments are carried out to compare and analyze by classifying three basic emotion of angry, sad and happy with neutral as emotionless state. Results reveal that the approach adopted has great potential to identify speech emotions with high classification rate and reasonable computation time.
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The affective space model (ASM) based on the valence and arousal (VA) has been used by many researchers in determining the emotional state of an individual. Psychologist uses the self assessment maniquin (SAM) while other researchers uses... more
The affective space model (ASM) based on the valence and arousal (VA) has been used by many researchers in determining the emotional state of an individual. Psychologist uses the self assessment maniquin (SAM) while other researchers uses the facial patterns, voice emotions and also electroencephalogram (EEG) signals to obtain the category of Sentiment analysis (SA) based on VA as the two dimensional approach represents affective state. However, getting affective words with VA scores are still infrequently used, even though these VA lexicon are advantageous resource in creating application of sentiment, especially in the Indonesian language and can be used as a corpus for SA. Thus this paper proposes to design and analyze Indonesian affective lexicons based on affective norm english word (ANEW) for automatic determination of VA rating of words. In this research, we proposed to develop an extensive number of sentiment states in Indonesian language that have been placed in terms of VA using SAM and would be correlated with EEG as a comprehensive tool of Neuro Physiological Signal for the emotion sentiment corpus rating.
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There had been many empirical researched demonstrating the important link between customer satisfaction and sales performance, as such many Customer Satisfaction index (CSI) were developed. Almost all CSI to date uses the survey or... more
There had been many empirical researched demonstrating the important link between customer satisfaction and sales performance, as such many Customer Satisfaction index (CSI) were developed. Almost all CSI to date uses the survey or questionnaire method, which has its flaws. In order to quantify the CSI, we propose the use of speech analysis based on the affective space model where the valence and arousal of the customer can be extracted, indicating their immediate emotion. Speech were recorded and relevant features extracted using Mel Frequency Cepstral Coefficient (MFCC) coupled with Adaptive Neuro Fuzzy Inference System (ANFIS) with subtractive clustering for classification. The network were trained to responds to the two dimensional Affective Space Model (ASM) classification which consist of valence and arousal. Further analysis were carried out to understand the impact of neutral on the accuracy of the classification. Experimental results show that recognition rate for measuring satisfaction is 40% and neutral emotion obtained the highest recognition with 58%. Such analysis can help in understanding the satisfaction and dissatisfaction of customers based on speech with improving the accuracy.
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Cultural differences have been one of the many factors that can cause failures in speech emotion analysis. If this cultural parameter could be regarded as noise artifacts in detecting emotion in speech, we could then extract pure emotion... more
Cultural differences have been one of the many factors that can cause failures in speech emotion analysis. If this cultural parameter could be regarded as noise artifacts in detecting emotion in speech, we could then extract pure emotion speech signal from the raw emotional ...
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Emotions are ambiguous. Many techniques have been employed to perform emotion prediction and to understand emotional elicitations. Brain signals measured using electroencephalogram (EEG) are also used in studies about emotions. Using KDE... more
Emotions are ambiguous. Many techniques have been employed to perform emotion prediction and to understand emotional elicitations. Brain signals measured using electroencephalogram (EEG) are also used in studies about emotions. Using KDE as feature extraction technique and MLP for performing supervised learning on the brain signals. It has shown that all channels in EEG can capture emotional experience. In addition it was also indicated that emotions are dynamic as represented by the level of valence and the intensity of arousal. Such findings are useful in biomedical studies, especially in dealing with emotional disorders which can results in using a two-channel EEG device for neurofeedback applications.
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ABSTRACT This paper proposes an emotion recognition system using the electroencephalographic (EEG) signals. Both time domain and frequency domain approaches for feature extraction were evaluated using neural network (NN) and fuzzy neural... more
ABSTRACT This paper proposes an emotion recognition system using the electroencephalographic (EEG) signals. Both time domain and frequency domain approaches for feature extraction were evaluated using neural network (NN) and fuzzy neural network (FNN) as classifiers. Data was collected using psychological stimulation experiments. Three basic emotions namely; Angry, Happy, and Sad were selected for recognition with relax as an emotionless state. Both the time domain (based on statistical method) and frequency domain (based on MFCC) approaches shows potential to be used for emotion recognition using the EEG signals.
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Driver behavior is indeed one of the major factors contributing to high number of motor vehicle accidents. Due to the fact that human behavior is always influenced by emotion and emotion can be detected through speech, we attempt to find... more
Driver behavior is indeed one of the major factors contributing to high number of motor vehicle accidents. Due to the fact that human behavior is always influenced by emotion and emotion can be detected through speech, we attempt to find correlation between driver behavior state and speech emotion to analyze driver behavior. This understanding is important to facilitate the development of driver emotional indicator system that can act as some kind of warning system to prevent accidents. Experimental results show potential for driver behavior state detection particularly for sleepy state based on speech emotion recognition approach coupled with fundamental understanding of affection space model. These findings surged us to propose an alternative approach of speech emotion profiling that complement the research mainstream of driver behavior and speech emotion recognition.
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Dysphoria is a trigger point for maladjusted individuals who cannot cope with disappointments and crushed expectations, resulting in negative emotions if it is not detected early. Individuals who suffer from dysphoria tend to deny their... more
Dysphoria is a trigger point for maladjusted individuals who cannot cope with disappointments and crushed expectations, resulting in negative emotions if it is not detected early. Individuals who suffer from dysphoria tend to deny their mental state. They try to hide, suppress, or ignore the symptoms, making one feel worse, unwanted, and unloved. Psychologists and psychiatrists identify dysphoria using standardized instruments like questionnaires and interviews. These methods can boast a high success rate. However, the limited number of trained psychologists and psychiatrists and the small number of health institutions focused on mental health limit access to early detection. In addition, the negative connotation and taboo about dysphoria discourage the public from openly seeking help. An alternative approach to collecting ‘pure’ data is proposed in this paper. The brain signals are captured using the electroencephalogram as the input to the machine learning approach to detect negat...
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The internet has revolutionized the way most people shop. Flexibility, convenience, products’ variations, better price, and more privacy contribute to the exponential growth of online shopping platforms. However, due to the nature of... more
The internet has revolutionized the way most people shop. Flexibility, convenience, products’ variations, better price, and more privacy contribute to the exponential growth of online shopping platforms. However, due to the nature of online shopping, customers are not able to physically test the product before purchasing. They rely on the information given by the seller and previous customers’ ratings to make their decision. Sometimes, the information that is given by sellers may be fraudulent, misleading, or over claim. Many researchers had found out that ratings and other customers’ reviews can be manipulated and did not reflect on the actual customers’ sentiment on the particular product. This research investigates how sentiment analysis can be used as an alternative solution to measure the positive, negative, and neutral feedback of the past reviews. It is to offer more comprehensive way to help the customers make an informed decision for the product that they wish to buy based ...
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Researchers have focused on the negative effects of stress while its benefits have been relatively ignored. There has been limited studies to quantitatively understand the positive impact of stress. Although most of the studies were... more
Researchers have focused on the negative effects of stress while its benefits have been relatively ignored. There has been limited studies to quantitatively understand the positive impact of stress. Although most of the studies were carried out by psychologist, in general, stress can be characterized by negative valence from the perspective of the affective state model (ASM). In fact, most recent psychological findings show that positive stress, also known as eustress, can improve motivation factor of an individual. In this paper we propose the use of electroencephalography (EEG) device to capture the brain's electrical activity in the frontal and central areas, in identifying positive (eustress) and negative (distress) stress. The distinctive brainwave patterns from the EEG device can be used to extract emotion/mood information of an individual and can be used to corelate the differing stress. The neurophysiological Model of affect (NPMoA) extracts the valence (V) and arousal (...
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Studies have shown that memory effectiveness can be greatly influenced by emotional arousal. In this paper relationship between EEG-based emotional arousal and memory effectiveness were investigated. The focus of the research work assumed... more
Studies have shown that memory effectiveness can be greatly influenced by emotional arousal. In this paper relationship between EEG-based emotional arousal and memory effectiveness were investigated. The focus of the research work assumed information encoding and information retrieval operations as memory effectiveness. Previous studies indicated a strong correlation between emotions and memory consolidation, while other studies reported crucial role of the amygdala in exerting emotional arousal influences. This role suggested seems to be greater during information encoding than retrieval. In this preliminary study four postgraduate students volunteered to participate in this memory test experiment, and their EEG brain wave signals were captured and recorded during training and testing. The results of processing their brain signals seem to indicate high level of emotional arousal, which may have strong correlation with memory consolidation. The results also support the notion that, ...