In the present world , the speech processing has become essential for technological developments in various aspects and this technology is also incorporated in many gadgets . Emotion-based recognition is where the emotion of the person is identified from the differences in stress and other properties of speech . The features such as intensity , formants , bandwidth , and pitch vary with the change in emotion . All such approaches have been done to raise the accuracy and appropriateness of emotion classification . In this study , a hybrid approach of audio and video has been applied for emotion recognition . The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification . Nowadays , recognizing human emotion is an important issue in the human-robot interaction field . Accordingly , in this paper a novel feature for emotion recognition is proposed . In particular , the emotion recognition system introduced in this study uses information extracted from human speech only . This algorithm is shown effective and useful in achieving both face identification and facial expression recognition . The performance and merit of the proposed methods are exemplified by conducting several experiments on face identification , emotion recognition and interactive scenarios . Abstract In this study , electroencephalography-based data for emotion recognition analysis are introduced . EEG signals were collected from 28 different subjects with a wearable and portable EEG device called the 14-channel EMOTIV EPOC+ . This is the first concrete investigation of emotion recognition capability or affective computing using a low-cost , open source electroencephalography ( EEG ) amplifier called OpenBCI . The most important aspect for this kind of study is effective emotional elicitation . An emotion recognition part(20 ) extracts a plurality of emotion feature variables in order to estimate a heart beat variation , respiration , a pulse wave speed , and tension estimation from the bio signal measured from the measuring part . The emotion recognition part recognizes an individual emotional state through the optimized emotion feature variables by executing individual optimization . A bio-signal storage part(30 ) is accessed by the emotion recognition part . EREGE system consists of two parts ; facial emotion recognition that recognizes seven emotions such as neutral , happiness , sadness , anger , disgust , fear , and surprise . In the emotion recognition part , we have implemented an Active Shape Model ( ASM ) tracker which tracks 116 facial landmarks via webcam input . The tracked landmark points are used to extract face expression features . However , it remains an important unanswered question whether or not these different stimulus formats entail the same processes . Facial expressions of emotion may elicit more emotion recognition/perception , and evocative pictures may elicit more direct experience of emotion . In spite of these differences , common areas of activation have been reported across different studies , but little work has investigated activations in response to the two stimulus formats in the same subjects . To solve emotion recognition problem for digital music speech , an emotion recognition method which is based on difference between characteristics and SVM voting mechanism is proposed . The method not only reduces the dimension of feature vectors , but retains the characteristics between different emotions of digital music speech . This paper introduces the CASIA audio emotion recognition method for the audio sub-challenge of Audio/Visual Emotion Challenge 2011 ( AVEC2011 ) . Two popular pattern recognition techniques , SVM and AdaBoost , are adopted to solve the emotion recognition problem . The feature set is also simply investigated by comparing the performance of classifier built on the baseline feature set and the dimension reduced feature set . The automatic recognition of emotional states are quite researched problem . In this work , emotion recognition problem was investigated and the system has been developed that can recognize expressions of happy , sad , surprised , angry , afraid , disgusted and neutral from an image . Previously , a feature vector was extracted for the detected face and the classification was made using the distance information between the vector and vectors of emotional expression . Recognizing an emotional context created using human bio-signals has gained traction in contemporary applications . The current emotional ontology however can not handle probabilistic information in the emotion recognition process . The primary goal of this research is to utilize a Bayesian Network into the study of EEG-based emotion recognition to address the probabilistic context data . No mood-congruity effects were found . The study sheds light on some of the factors underlying the emotion-recognition process . Possible explanations of the contribution of attachment style on the ability to decode vocal stimuli were discussed . We propose the use of the popular error correcting codes ( ECC ) in a multi-class audio emotion recognition scenario to improve the emotion recognition accuracy in spoken speech . In this paper , we visualize the emotion recognition system as a noisy communication channel , thus motivating the use of ECC in the emotion recognition process . We assume the emotion recognition process consists of an audio feature extraction module followed by an artificial neural network ( ANN ) for emotion ( represented by a binary string ) classification . The system is able to recognize six emotions(anger , boredom , disgust , fear , happiness , and sadness ) and the neutral state . This set of emotional states is widely used for emotion recognition purposes . It also distinguishes a single emotion versus all the other possible ones , as proven in the proposed numerical results . In order to develop more precise and functional affective applications , it is necessary to achieve a balance between the psychology and the engineering applied to emotions . Signals from the central and peripheral nervous systems have been used for emotion recognition purposes , however , their operation and the relationship between them remains unknown . In this context , in the present work , we have tried to approach the study of the psychobiology of both systems in order to generate a computational model for the recognition of emotions in the dimension of valence . The proposed method is able to recognize six emotional states , such as joy , happiness , fear , anger , despair , and sadness . These set of emotional states are widely used for emotion recognition purposes . The result shows that the proposed method can distinguish one emotion compared to all other possible emotional states . On the CKP set the current state of the art approach , using CNNs , achieves an accuracy of 99.2 % . For the MMI dataset , currently the best accuracy for emotion recognition is 93.33 % . The proposed architecture achieves % for CKP and % for MMI , therefore performing better than the state of the art using CNNs . With a 10 - fold cross - validation a recognition accuracy of 99.6 % has been achieved . The paper is arranged as follows : After this introduction , Related Work ( Section [ reference ] ) is presented which focuses on Emotion / Expression recognition and the various approaches scientists have taken . Next is Section [ reference ] , Background , which focuses on the main components of the architecture proposed in this article . Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance . In this paper , we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification . Our model outperforms the state of the art by a significant margin on two different datasets . Our method outperforms the widely used early fusion on both datasets typically used to test multimodal sentiment analysis methods . Moreover , with the addition of context modeling with GRU , our method outperforms the state of the art in multimodal sentiment analysis and emotion detection by significant margin . In our future work , we plan to improve the quality of unimodal features , especially textual features , which will further improve the accuracy of classification . Meanwhile on the social media dataset , our approach outperforms commercially available APIs by significant margins , even when those models are re - calibrated to the test set . Furthermore , we notice that 1 ) the Transformer model generally out - performs the mLSTM model , especially when fine - tuning on multidimensional emotion classification , and 2 ) fine - tuning the model significantly improves performance on the emotion tasks , both for the mLSTM and the Transformer model . We suggest that our approach creates models with good generalization to increasingly difficult text classification problems , and we offer ablation studies to demonstrate that effect . The published papers are of high quality and represent both theoretical and application favors . Among these 9 papers , three are on NLP based knowledge abstraction , two are on man-machine communication and emotion based conversation , two are on language learning , one is on robot and one is on data fusion . We would like to take this opportunity to thank all the contributors and the reviewers for their contribution and devoted efforts . The electroencephalogram ( EEG ) has great attraction in emotion recognition studies due to its resistance to deceptive actions of humans . This is one of the most significant advantages of brain signals in comparison to visual or speech signals in the emotion recognition context . A major challenge in EEG-based emotion recognition is that EEG recordings exhibit varying distributions for different people as well as for the same person at different time instances . Emotion recognition in conversation ( ERC ) has received much attention , lately , from researchers due to its potential widespread applications in diverse areas , such as health-care , education , and human resources . In this paper , we present Dialogue Graph Convolutional Network ( DialogueGCN ) , a graph neural network based approach to ERC . Emotion is intrinsic to humans and consequently , emotion understanding is a key part of human-like artificial intelligence ( AI ) . Emotion recognition in conversation ( ERC ) is becoming increasingly popular as a new research frontier in natural language processing ( NLP ) due to its ability to mine opinions from the plethora of publicly available conversational data on platforms such as Facebook , Youtube , Reddit , Twitter , and others . Moreover , it has potential applications in health-care systems ( as a tool for psychological analysis ) , education ( understanding student frustration ) , and more . In this research we look at ways for improving existing AI techniques by the use of associative neural networks , proposed by Haikonen for machine consciousness . We find that all examined technologies do profit from such an approach : speech recognition , emotion recognition in speech , EMG data analysis for multilingual speech processing , the simulation of bistable perception and the generation of random numbers . EMG data analysis for multilingual speech processing ( silent speech recognition ) is selected as the main example in this paper for its simple yet complete architecture . Specifically , it investigates the potential applications of information processing and natural intelligence to science and engineering disciplines . This collection , entitled Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence provides emerging research topics in cognitive informatics research with a focus on such topics as reducing cognitive overload , real-time process algebra , and neural networks for iris recognition , emotion recognition in speech , and the classification of musical chords . In current studies , only few emotion recognition systems considered nonverbal sounds , such as laughter , cries or other emotion interjection , which naturally exists in our daily conversation . In this work , both verbal and nonverbal sounds within an utterance were thus considered for emotion recognition of real-life conversations . Firstly , an SVM-based verbal/nonverbal sound detector was developed . The training phase firstly adopts emotion classification to classify the emotion feature vector , then conducts model train and finally builds the emotion speech base ; and the recognition phase adopts semantic analysis and pragmatics analysis to conduct natural language comprehension on the emotion feature vector . The emotion speech recognition method is strong in naturalness of the emotion speech recognition , can integrally comprehend the emotion speech signals , and improves recognition rate of the emotion speech signals . Though emotion speech recognition has gained increasing interest in the field of Human Computer Interaction , it is still a challenge to automatically determine the emotion state type and the boundaries of each emotionally salient segment in continuous speech , which is named as Automatic Emotion Variation Detection ( AEVD ) . In this task , the input utterances are not pre-segmented and may contain emotion variations . Abstract We examined the categorical nature of emotion word recognition . Positive , negative , and neutral words were presented in lexical decision tasks . It is well known that the performance of emotional talking condition recognition is imperfect . This work aims at enhancing the performance of emotional talking condition recognition based on " Third-Order Hidden Markov Models ( HMM3s ) " as classifiers . Our work has been tested on " Emotional Prosody Speech and Transcripts ( EPST ) " database . The extracted features of EPST database are Mel-Frequency Cepstral Coefficients ( MFCCs ) . Our results give average talking recognition performance of 71.8 % . The results of this work show that HMM3s are superior to First-Order Hidden Markov Models ( HMM1s ) and Second-Order Hidden Markov Models ( HMM2s ) by 14.0 % and 5.7 % , respectively , for emotional talking condition recognition . The average talking recognition performance attained based on HMM3s is very close to that obtained using " subjective assessment by human judges " . A speech emotion recognition algorithm based on multi-feature and Multi-lingual fusion is proposed in order to resolve low recognition accuracy caused by lack of large speech dataset and low robustness of acoustic features in the recognition of speech emotion . First , handcrafted and deep automatic features are extracted from existing data in Chinese and English speech emotions . The designed networks , combinations of the convolutional neural network ( CNN ) and LSTM , can take advantage of the strengths of both networks and overcome the shortcomings of them , and are evaluated on two benchmark databases . The experimental results show that the designed networks achieve excellent performance on the task of recognizing speech emotion , especially the 2D CNN LSTM network outperforms the traditional approaches , Deep Belief Network ( DBN ) and CNN on the selected databases . The 2D CNN LSTM network achieves recognition accuracies of 95.33 % and 95.89 % on Berlin EmoDB of speaker-dependent and speaker-independent experiments respectively , which compare favourably to the accuracy of 91.6 % and 92.9 % obtained by traditional approaches ; and also yields recognition accuracies of 89.16 % and 52.14 % on IEMOCAP database of speaker-dependent and speaker-independent experiments , which are much higher than the accuracy of 73.78 % and 40.02 % obtained by DBN and CNN . Unlike CCA and Discriminative CCA , the proposed DDCCA does not need inner product . The proposed DDCCA was evaluated in two applications , handwritten digit recognition and speech-based emotion recognition . The experimental results demonstrated that the proposed DDCCA can get a higher recognition accuracy compared to the existing Deep CCA method . Speech emotion recognition has recently attracted much interest due to the widespread of multimedia data . It generally involves two basic problems : feature extraction and emotion classification . The selection of feature subset is a crucial aspect in speech emotion recognition problem . In this paper , a Reordering Features with Weights Fusion ( RFWF ) algorithm is proposed for selecting more effective and compact feature subset . Also , the state of support — supportive , negative and unknown , are inferred . In addition to listening experiments on the emotions and support states , the application of the analysis of the speech emotion recognition process is discussed on the basis of verifying the emotion of speech and the dependence of the support state . The accuracy of the average objective recognition rate can be increased to 75 % in the formation of the consensus during speech conversation . Abstract This paper pulls together the advances of recognizing emotion theory with advances in speech feature in order to improve understand of emotion under real life condition . It presents the application of a recently proposed feature extraction method based on spectral features , used for speech emotion recognition purposes . Specifically , the performance of the proposed approach is evaluated on real condition speech signal ( IEMOCAP database ) with real world noise using various SNR levels . One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy . In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods , such as the Fisher and PCA . A Pattern Analysis Approach . emotion recognition a pattern analysis approach helano emotion recognition : a pattern analysis approach emotion recognition a pattern analysis approach human emotion recognition by using pattern ijera emotion recognition based on audio speech iosr journals a regression approach to music emotion recognition pattern recognition in narrative : analysis of narratives a dynamic recognition approach of emotional states for car a multi-level classification approach for facial emotion emotion recognition : a step ahead of traditional approaches emotion recognition a pattern analysis approach pdf format emotion recognition from expressions in face , voice , and survey paper on emotion recognition ijeas multimodal approaches for emotion recognition : a survey emotion recognition from eeg during self-paced emotional deep learning for emotion recognition on small datasets speech emotion recognition : a review manifolds based emotion recognition in speech a new approach to emotion recognition from the lip contour text based emotion recognition : a survey emotion from facial expression recognition upv/ehu toward emotion recognition in car-racing drivers : a facial emotion recognition in schizophrenia : intensity emotion recognition with image processing and neural improving speech emotion recognition using adaptive limitations of emotion recognition in software user analysis of emotion recognition system through speech facial emotion recognition in continuous video vislab an experimental study of speech emotion recognition based recognition of emotions in autism : a formal meta-analysis survey on rgb , 3d , thermal , and multimodal approaches for multiscale local binary patterns for facial expression facial emotion recognition tecfa bodily expression for automatic affect recognition identifying emotion by keystroke dynamics and text pattern emotion recognition from users ’ eeg signals with the help noise analysis in audio-visual emotion recognition compensating for speaker or lexical variabilities in emotion recognition in adolescents with down syndrome : a emotion recognition system for english dialects using d4.6 emotion recognition from image and video content automatic emotion recognition using facial expression : a During this interaction , human beings have some feelings that they want to convey to their communication partner with whom they are communicating , and then their communication partner may be the human or machine . This work dependent on the emotion recognition of the human beings from their speech signal Emotion recognition from the speaker ’s speech is very difficult because of the following reasons : Because of the existence of the different sentences , speakers , speaking styles , speaking rates accosting variability was introduced . The same utterance may show different emotions . While this improvement has been significant , deep learning still plays only a limited role in traditional speech pipelines . As a result , to improve performance on a task such as recognizing speech in a noisy environment , one must laboriously engineer the rest of the system for robustness . In contrast , our system applies deep learning end - to - end using recurrent neural networks . TomSercu ChristianPuhrsch BrianKingsbury YannLeCun CenterforDataScience , CourantInstituteofMathematicalSciences , NewYorkUniversity IBMT.J.WatsonResearchCenter , YorktownHeights , NY , 10598 , U.S.A. { tsercu , bedk } @us.ibm.com , 1cpuhrsch@nyu.edu , yann@cs.nyu.edu Convolutional Networks , Multilingual , Acoustic Modeling , Speech Recognition , Neural Networks section : INTRODUCTION Computational costs and numerical instability led to these techniques to be abandoned for almost a decade . Recently , distributed computing allowed for larger models to be trained [ reference ] , and the growth of data for unsupervised learning algorithms to emerge [ reference ] . Distributed representations usually are trained through neural networks , these networks have made advancements in diverse fields such as computer vision [ reference ] , speech recognition [ reference ] , and natural language processing [ reference ] . section : CONCLUSIONS section : Conclusions In this paper , we revised standard GRUs for speech recognition purposes . The proposed Li - GRU architecture is a simplified version of a standard GRU , in which the reset gate is removed and ReLU activations are considered . Automatic emotion recognition in the wild video datasets is a very challenging problem because of the inter-class similarities among different facial expressions and large intra-class variabilities due to the significant changes in illumination , pose , scene , and expression . In this paper , we present our proposed method for video-based emotion recognition in the EmotiW 2016 challenge . The task considers the unconstrained emotion recognition problem by training from short video clips extracted from movies and testing on short movie clips and spontaneous video clips of the reality TV data . The study of video-based emotion recognition is still a difficult and hot research issue in the area of computer vision and human-computer interaction . How to effectively extract key frames from videos , construct spatiotemporal feature space of videos , and build model based on the mapping relationship between temporal feature space and video emotion type space , has become an important issue of video-based emotion recognition study . To solve this problem , this paper proposes a video emotion recognition method based on the attention mechanism LSTM model . In this paper , we propose a method for video-based human emotion recognition . For each video clip , all frames are represented as an image set , which can be modeled as a linear subspace to be embedded in Grassmannian manifold . In this way , static image features extracted from a pre-trained deep CNN and temporal information represented by DFT features are jointly considered for video classification . We test our method for video emotion classification and action recognition . Experimental results demonstrate that combining DFT features can effectively capture temporal information and therefore improve the performance of both video emotion classification and action recognition . Beside these , which are mostly used,[13 ] report the usage of other facial features in relatedwork : active appearance models ( AAM ) , action units , various facial points and motion units , Haar based fea-tures and textures . Various classiﬁcation schemes were used success-fully in video emotion detection . [ 1 ] employed both theSupport Vector Machine ( SVM ) and AdaBoost classi-ﬁers . On the one hand , traditional motion history image method is improved by using detected human facial landmarks as attention areas to boost local value in difference image calculation , so that the action of crucial facial unit can be captured effectively , then the generated LEMHI is fed into a CNN network for categorization . On the other hand , a CNN-LSTM model is used as an global feature extractor and classifier for video emotion recognition . Finally , a random search weighted summation strategy is selected as our late-fusion fashion to final predication . A novel content-based retrieval algorithm is presented for video emotion types recognition in this paper . Considering the fuzzyness and uncertainty of the human 's cognitive behavior , we established the relation between low-level video features and high-level emotion types by the unascertained theory . Understanding video content is a challenging problem in many applications , especially for emotion analysis . Diverse and complicated video contents are the major obstacles in video emotion understanding . In this paper , we propose a modality fusion framework to combine concept and content features from action , scene and object models . Then this single frame will be fed into a CNN network for prediction . On the other hand , an improved CNN-LSTM model is used as a global feature extractor and classifier for video facial emotion recognition in the global network . Finally , a random search weighted summation strategy is conducted as late-fusion fashion to final predication . In this paper , a few video retrieval problems are studied at the view of affective content recognition . Based on the unascertained theory , the relation between low-level video emotion features and highlevel cognitive emotion is established , and a novel algorithm about video scene emotion type recognition , which is based on the unascertained measure , is proposed . The algorithm consists of two parts . The average accuracy is 94.09 % . Video based emotion recognition has been proposed by Byeon and Kwak . They have developed a three dimensional CNN which uses groups of 5 consecutive frames as input .
In the present world , the speech processing has become essential for technological developments in various aspects and this technology is also incorporated in many gadgets . Emotion-based recognition is where the emotion of the person is identified from the differences in stress and other properties of speech . The features such as intensity , formants , bandwidth , and pitch vary with the change in emotion .
All such approaches have been done to raise the accuracy and appropriateness of emotion classification . In this study , a hybrid approach of audio and video has been applied for emotion recognition . The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification .
Nowadays , recognizing human emotion is an important issue in the human-robot interaction field . Accordingly , in this paper a novel feature for emotion recognition is proposed . In particular , the emotion recognition system introduced in this study uses information extracted from human speech only .
This algorithm is shown effective and useful in achieving both face identification and facial expression recognition . The performance and merit of the proposed methods are exemplified by conducting several experiments on face identification , emotion recognition and interactive scenarios .
Abstract In this study , electroencephalography-based data for emotion recognition analysis are introduced . EEG signals were collected from 28 different subjects with a wearable and portable EEG device called the 14-channel EMOTIV EPOC+ .
This is the first concrete investigation of emotion recognition capability or affective computing using a low-cost , open source electroencephalography ( EEG ) amplifier called OpenBCI . The most important aspect for this kind of study is effective emotional elicitation .
An emotion recognition part(20 ) extracts a plurality of emotion feature variables in order to estimate a heart beat variation , respiration , a pulse wave speed , and tension estimation from the bio signal measured from the measuring part . The emotion recognition part recognizes an individual emotional state through the optimized emotion feature variables by executing individual optimization . A bio-signal storage part(30 ) is accessed by the emotion recognition part .
EREGE system consists of two parts ; facial emotion recognition that recognizes seven emotions such as neutral , happiness , sadness , anger , disgust , fear , and surprise . In the emotion recognition part , we have implemented an Active Shape Model ( ASM ) tracker which tracks 116 facial landmarks via webcam input . The tracked landmark points are used to extract face expression features .
However , it remains an important unanswered question whether or not these different stimulus formats entail the same processes . Facial expressions of emotion may elicit more emotion recognition/perception , and evocative pictures may elicit more direct experience of emotion . In spite of these differences , common areas of activation have been reported across different studies , but little work has investigated activations in response to the two stimulus formats in the same subjects .
To solve emotion recognition problem for digital music speech , an emotion recognition method which is based on difference between characteristics and SVM voting mechanism is proposed . The method not only reduces the dimension of feature vectors , but retains the characteristics between different emotions of digital music speech .
This paper introduces the CASIA audio emotion recognition method for the audio sub-challenge of Audio/Visual Emotion Challenge 2011 ( AVEC2011 ) . Two popular pattern recognition techniques , SVM and AdaBoost , are adopted to solve the emotion recognition problem . The feature set is also simply investigated by comparing the performance of classifier built on the baseline feature set and the dimension reduced feature set .
The automatic recognition of emotional states are quite researched problem . In this work , emotion recognition problem was investigated and the system has been developed that can recognize expressions of happy , sad , surprised , angry , afraid , disgusted and neutral from an image . Previously , a feature vector was extracted for the detected face and the classification was made using the distance information between the vector and vectors of emotional expression .
Recognizing an emotional context created using human bio-signals has gained traction in contemporary applications . The current emotional ontology however can not handle probabilistic information in the emotion recognition process . The primary goal of this research is to utilize a Bayesian Network into the study of EEG-based emotion recognition to address the probabilistic context data .
No mood-congruity effects were found . The study sheds light on some of the factors underlying the emotion-recognition process . Possible explanations of the contribution of attachment style on the ability to decode vocal stimuli were discussed .
We propose the use of the popular error correcting codes ( ECC ) in a multi-class audio emotion recognition scenario to improve the emotion recognition accuracy in spoken speech . In this paper , we visualize the emotion recognition system as a noisy communication channel , thus motivating the use of ECC in the emotion recognition process . We assume the emotion recognition process consists of an audio feature extraction module followed by an artificial neural network ( ANN ) for emotion ( represented by a binary string ) classification .
The system is able to recognize six emotions(anger , boredom , disgust , fear , happiness , and sadness ) and the neutral state . This set of emotional states is widely used for emotion recognition purposes . It also distinguishes a single emotion versus all the other possible ones , as proven in the proposed numerical results .
In order to develop more precise and functional affective applications , it is necessary to achieve a balance between the psychology and the engineering applied to emotions . Signals from the central and peripheral nervous systems have been used for emotion recognition purposes , however , their operation and the relationship between them remains unknown . In this context , in the present work , we have tried to approach the study of the psychobiology of both systems in order to generate a computational model for the recognition of emotions in the dimension of valence .
The proposed method is able to recognize six emotional states , such as joy , happiness , fear , anger , despair , and sadness . These set of emotional states are widely used for emotion recognition purposes . The result shows that the proposed method can distinguish one emotion compared to all other possible emotional states .
On the CKP set the current state of the art approach , using CNNs , achieves an accuracy of 99.2 % . For the MMI dataset , currently the best accuracy for emotion recognition is 93.33 % . The proposed architecture achieves % for CKP and % for MMI , therefore performing better than the state of the art using CNNs .
With a 10 - fold cross - validation a recognition accuracy of 99.6 % has been achieved . The paper is arranged as follows : After this introduction , Related Work ( Section [ reference ] ) is presented which focuses on Emotion / Expression recognition and the various approaches scientists have taken . Next is Section [ reference ] , Background , which focuses on the main components of the architecture proposed in this article .
Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance . In this paper , we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification . Our model outperforms the state of the art by a significant margin on two different datasets .
Our method outperforms the widely used early fusion on both datasets typically used to test multimodal sentiment analysis methods . Moreover , with the addition of context modeling with GRU , our method outperforms the state of the art in multimodal sentiment analysis and emotion detection by significant margin . In our future work , we plan to improve the quality of unimodal features , especially textual features , which will further improve the accuracy of classification .
Meanwhile on the social media dataset , our approach outperforms commercially available APIs by significant margins , even when those models are re - calibrated to the test set . Furthermore , we notice that 1 ) the Transformer model generally out - performs the mLSTM model , especially when fine - tuning on multidimensional emotion classification , and 2 ) fine - tuning the model significantly improves performance on the emotion tasks , both for the mLSTM and the Transformer model . We suggest that our approach creates models with good generalization to increasingly difficult text classification problems , and we offer ablation studies to demonstrate that effect .
The published papers are of high quality and represent both theoretical and application favors . Among these 9 papers , three are on NLP based knowledge abstraction , two are on man-machine communication and emotion based conversation , two are on language learning , one is on robot and one is on data fusion . We would like to take this opportunity to thank all the contributors and the reviewers for their contribution and devoted efforts .
The electroencephalogram ( EEG ) has great attraction in emotion recognition studies due to its resistance to deceptive actions of humans . This is one of the most significant advantages of brain signals in comparison to visual or speech signals in the emotion recognition context . A major challenge in EEG-based emotion recognition is that EEG recordings exhibit varying distributions for different people as well as for the same person at different time instances .
Emotion recognition in conversation ( ERC ) has received much attention , lately , from researchers due to its potential widespread applications in diverse areas , such as health-care , education , and human resources . In this paper , we present Dialogue Graph Convolutional Network ( DialogueGCN ) , a graph neural network based approach to ERC .
Emotion is intrinsic to humans and consequently , emotion understanding is a key part of human-like artificial intelligence ( AI ) . Emotion recognition in conversation ( ERC ) is becoming increasingly popular as a new research frontier in natural language processing ( NLP ) due to its ability to mine opinions from the plethora of publicly available conversational data on platforms such as Facebook , Youtube , Reddit , Twitter , and others . Moreover , it has potential applications in health-care systems ( as a tool for psychological analysis ) , education ( understanding student frustration ) , and more .
In this research we look at ways for improving existing AI techniques by the use of associative neural networks , proposed by Haikonen for machine consciousness . We find that all examined technologies do profit from such an approach : speech recognition , emotion recognition in speech , EMG data analysis for multilingual speech processing , the simulation of bistable perception and the generation of random numbers . EMG data analysis for multilingual speech processing ( silent speech recognition ) is selected as the main example in this paper for its simple yet complete architecture .
Specifically , it investigates the potential applications of information processing and natural intelligence to science and engineering disciplines . This collection , entitled Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence provides emerging research topics in cognitive informatics research with a focus on such topics as reducing cognitive overload , real-time process algebra , and neural networks for iris recognition , emotion recognition in speech , and the classification of musical chords .
In current studies , only few emotion recognition systems considered nonverbal sounds , such as laughter , cries or other emotion interjection , which naturally exists in our daily conversation . In this work , both verbal and nonverbal sounds within an utterance were thus considered for emotion recognition of real-life conversations . Firstly , an SVM-based verbal/nonverbal sound detector was developed .
The training phase firstly adopts emotion classification to classify the emotion feature vector , then conducts model train and finally builds the emotion speech base ; and the recognition phase adopts semantic analysis and pragmatics analysis to conduct natural language comprehension on the emotion feature vector . The emotion speech recognition method is strong in naturalness of the emotion speech recognition , can integrally comprehend the emotion speech signals , and improves recognition rate of the emotion speech signals .
Though emotion speech recognition has gained increasing interest in the field of Human Computer Interaction , it is still a challenge to automatically determine the emotion state type and the boundaries of each emotionally salient segment in continuous speech , which is named as Automatic Emotion Variation Detection ( AEVD ) . In this task , the input utterances are not pre-segmented and may contain emotion variations .
Abstract We examined the categorical nature of emotion word recognition . Positive , negative , and neutral words were presented in lexical decision tasks .
It is well known that the performance of emotional talking condition recognition is imperfect . This work aims at enhancing the performance of emotional talking condition recognition based on " Third-Order Hidden Markov Models ( HMM3s ) " as classifiers . Our work has been tested on " Emotional Prosody Speech and Transcripts ( EPST ) " database . The extracted features of EPST database are Mel-Frequency Cepstral Coefficients ( MFCCs ) . Our results give average talking recognition performance of 71.8 % . The results of this work show that HMM3s are superior to First-Order Hidden Markov Models ( HMM1s ) and Second-Order Hidden Markov Models ( HMM2s ) by 14.0 % and 5.7 % , respectively , for emotional talking condition recognition . The average talking recognition performance attained based on HMM3s is very close to that obtained using " subjective assessment by human judges " .
A speech emotion recognition algorithm based on multi-feature and Multi-lingual fusion is proposed in order to resolve low recognition accuracy caused by lack of large speech dataset and low robustness of acoustic features in the recognition of speech emotion . First , handcrafted and deep automatic features are extracted from existing data in Chinese and English speech emotions .
The designed networks , combinations of the convolutional neural network ( CNN ) and LSTM , can take advantage of the strengths of both networks and overcome the shortcomings of them , and are evaluated on two benchmark databases . The experimental results show that the designed networks achieve excellent performance on the task of recognizing speech emotion , especially the 2D CNN LSTM network outperforms the traditional approaches , Deep Belief Network ( DBN ) and CNN on the selected databases . The 2D CNN LSTM network achieves recognition accuracies of 95.33 % and 95.89 % on Berlin EmoDB of speaker-dependent and speaker-independent experiments respectively , which compare favourably to the accuracy of 91.6 % and 92.9 % obtained by traditional approaches ; and also yields recognition accuracies of 89.16 % and 52.14 % on IEMOCAP database of speaker-dependent and speaker-independent experiments , which are much higher than the accuracy of 73.78 % and 40.02 % obtained by DBN and CNN .
Unlike CCA and Discriminative CCA , the proposed DDCCA does not need inner product . The proposed DDCCA was evaluated in two applications , handwritten digit recognition and speech-based emotion recognition . The experimental results demonstrated that the proposed DDCCA can get a higher recognition accuracy compared to the existing Deep CCA method .
Speech emotion recognition has recently attracted much interest due to the widespread of multimedia data . It generally involves two basic problems : feature extraction and emotion classification .
The selection of feature subset is a crucial aspect in speech emotion recognition problem . In this paper , a Reordering Features with Weights Fusion ( RFWF ) algorithm is proposed for selecting more effective and compact feature subset .
Also , the state of support — supportive , negative and unknown , are inferred . In addition to listening experiments on the emotions and support states , the application of the analysis of the speech emotion recognition process is discussed on the basis of verifying the emotion of speech and the dependence of the support state . The accuracy of the average objective recognition rate can be increased to 75 % in the formation of the consensus during speech conversation .
Abstract This paper pulls together the advances of recognizing emotion theory with advances in speech feature in order to improve understand of emotion under real life condition . It presents the application of a recently proposed feature extraction method based on spectral features , used for speech emotion recognition purposes . Specifically , the performance of the proposed approach is evaluated on real condition speech signal ( IEMOCAP database ) with real world noise using various SNR levels .
One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy . In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods , such as the Fisher and PCA .
A Pattern Analysis Approach . emotion recognition a pattern analysis approach helano emotion recognition : a pattern analysis approach emotion recognition a pattern analysis approach human emotion recognition by using pattern ijera emotion recognition based on audio speech iosr journals a regression approach to music emotion recognition pattern recognition in narrative : analysis of narratives a dynamic recognition approach of emotional states for car a multi-level classification approach for facial emotion emotion recognition : a step ahead of traditional approaches emotion recognition a pattern analysis approach pdf format emotion recognition from expressions in face , voice , and survey paper on emotion recognition ijeas multimodal approaches for emotion recognition : a survey emotion recognition from eeg during self-paced emotional deep learning for emotion recognition on small datasets speech emotion recognition : a review manifolds based emotion recognition in speech a new approach to emotion recognition from the lip contour text based emotion recognition : a survey emotion from facial expression recognition upv/ehu toward emotion recognition in car-racing drivers : a facial emotion recognition in schizophrenia : intensity emotion recognition with image processing and neural improving speech emotion recognition using adaptive limitations of emotion recognition in software user analysis of emotion recognition system through speech facial emotion recognition in continuous video vislab an experimental study of speech emotion recognition based recognition of emotions in autism : a formal meta-analysis survey on rgb , 3d , thermal , and multimodal approaches for multiscale local binary patterns for facial expression facial emotion recognition tecfa bodily expression for automatic affect recognition identifying emotion by keystroke dynamics and text pattern emotion recognition from users ’ eeg signals with the help noise analysis in audio-visual emotion recognition compensating for speaker or lexical variabilities in emotion recognition in adolescents with down syndrome : a emotion recognition system for english dialects using d4.6 emotion recognition from image and video content automatic emotion recognition using facial expression : a
During this interaction , human beings have some feelings that they want to convey to their communication partner with whom they are communicating , and then their communication partner may be the human or machine . This work dependent on the emotion recognition of the human beings from their speech signal Emotion recognition from the speaker ’s speech is very difficult because of the following reasons : Because of the existence of the different sentences , speakers , speaking styles , speaking rates accosting variability was introduced . The same utterance may show different emotions .
While this improvement has been significant , deep learning still plays only a limited role in traditional speech pipelines . As a result , to improve performance on a task such as recognizing speech in a noisy environment , one must laboriously engineer the rest of the system for robustness . In contrast , our system applies deep learning end - to - end using recurrent neural networks .
TomSercu ChristianPuhrsch BrianKingsbury YannLeCun CenterforDataScience , CourantInstituteofMathematicalSciences , NewYorkUniversity IBMT.J.WatsonResearchCenter , YorktownHeights , NY , 10598 , U.S.A. { tsercu , bedk } @us.ibm.com , 1cpuhrsch@nyu.edu , yann@cs.nyu.edu Convolutional Networks , Multilingual , Acoustic Modeling , Speech Recognition , Neural Networks section : INTRODUCTION
Computational costs and numerical instability led to these techniques to be abandoned for almost a decade . Recently , distributed computing allowed for larger models to be trained [ reference ] , and the growth of data for unsupervised learning algorithms to emerge [ reference ] . Distributed representations usually are trained through neural networks , these networks have made advancements in diverse fields such as computer vision [ reference ] , speech recognition [ reference ] , and natural language processing [ reference ] . section : CONCLUSIONS
section : Conclusions In this paper , we revised standard GRUs for speech recognition purposes . The proposed Li - GRU architecture is a simplified version of a standard GRU , in which the reset gate is removed and ReLU activations are considered .
Automatic emotion recognition in the wild video datasets is a very challenging problem because of the inter-class similarities among different facial expressions and large intra-class variabilities due to the significant changes in illumination , pose , scene , and expression . In this paper , we present our proposed method for video-based emotion recognition in the EmotiW 2016 challenge . The task considers the unconstrained emotion recognition problem by training from short video clips extracted from movies and testing on short movie clips and spontaneous video clips of the reality TV data .
The study of video-based emotion recognition is still a difficult and hot research issue in the area of computer vision and human-computer interaction . How to effectively extract key frames from videos , construct spatiotemporal feature space of videos , and build model based on the mapping relationship between temporal feature space and video emotion type space , has become an important issue of video-based emotion recognition study . To solve this problem , this paper proposes a video emotion recognition method based on the attention mechanism LSTM model .
In this paper , we propose a method for video-based human emotion recognition . For each video clip , all frames are represented as an image set , which can be modeled as a linear subspace to be embedded in Grassmannian manifold .
In this way , static image features extracted from a pre-trained deep CNN and temporal information represented by DFT features are jointly considered for video classification . We test our method for video emotion classification and action recognition . Experimental results demonstrate that combining DFT features can effectively capture temporal information and therefore improve the performance of both video emotion classification and action recognition .
Beside these , which are mostly used,[13 ] report the usage of other facial features in relatedwork : active appearance models ( AAM ) , action units , various facial points and motion units , Haar based fea-tures and textures . Various classiﬁcation schemes were used success-fully in video emotion detection . [ 1 ] employed both theSupport Vector Machine ( SVM ) and AdaBoost classi-ﬁers .
On the one hand , traditional motion history image method is improved by using detected human facial landmarks as attention areas to boost local value in difference image calculation , so that the action of crucial facial unit can be captured effectively , then the generated LEMHI is fed into a CNN network for categorization . On the other hand , a CNN-LSTM model is used as an global feature extractor and classifier for video emotion recognition . Finally , a random search weighted summation strategy is selected as our late-fusion fashion to final predication .
A novel content-based retrieval algorithm is presented for video emotion types recognition in this paper . Considering the fuzzyness and uncertainty of the human 's cognitive behavior , we established the relation between low-level video features and high-level emotion types by the unascertained theory .
Understanding video content is a challenging problem in many applications , especially for emotion analysis . Diverse and complicated video contents are the major obstacles in video emotion understanding . In this paper , we propose a modality fusion framework to combine concept and content features from action , scene and object models .
Then this single frame will be fed into a CNN network for prediction . On the other hand , an improved CNN-LSTM model is used as a global feature extractor and classifier for video facial emotion recognition in the global network . Finally , a random search weighted summation strategy is conducted as late-fusion fashion to final predication .
In this paper , a few video retrieval problems are studied at the view of affective content recognition . Based on the unascertained theory , the relation between low-level video emotion features and highlevel cognitive emotion is established , and a novel algorithm about video scene emotion type recognition , which is based on the unascertained measure , is proposed . The algorithm consists of two parts .
The average accuracy is 94.09 % . Video based emotion recognition has been proposed by Byeon and Kwak . They have developed a three dimensional CNN which uses groups of 5 consecutive frames as input .
Analysis of Emotion Recognition System for Telugu Using Prosodic and Formant Features Proposing A Hybrid Approach for Emotion Classification using Audio and Video Data Robust Speech Emotion Recognition Using Log Frequency Power Ratio Interactive emotion recognition using Support Vector Machine for human-robot interaction Database for an emotion recognition system based on EEG signals and various computer games - GAMEEMO EDOSE: Emotion Datasets from Open Source EEG with a Real-Time Bracelet Sensor Individual optimization system of recognizing emotion apparatus, method thereof Emotion Recognition and Eye Gaze Estimation System: EREGE Facial expressions and complex IAPS pictures: Common and differential networks An emotion recognition algorithm for digital music speech based on difference between characteristics and SVM voting mechanism The CASIA Audio Emotion Recognition Method for Audio/Visual Emotion Challenge 2011 Image-based facial expression detection A Bayesian Network (BN) Based Probabilistic Solution to Enhance Emotional Ontology The Influence of the Attachment Style on the Decoding Accuracy of Emotional Vocal Expressions Improved speech emotion recognition using error correcting codes Gender-Driven Emotion Recognition Through Speech Signals For Ambient Intelligence Applications Brain and Body Emotional Responses: Multimodal Approximation for Valence Classification Emotion extraction based on multi bio-signal using back-propagation neural network DeXpression: Deep Convolutional Neural Network for Expression Recognition DialogueRNN: An Attentive RNN for Emotion Detection in Conversations Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling Practical Text Classification With Large Pre-Trained Language Models SPECIAL ISSUE ON NATURAL LANGUAGE PROCESSING AND KNOWLEDGE ENGINEERING Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances Associative neural networks for machine consciousness: Improving existing AI technologies Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence (Advances in Cognitive Informatics and Natural Intelligence (Acini) Book Series) Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds Emotion speech recognition method based on natural language comprehension Automatic Emotion Variation Detection in continuous speech Emotion words and categories: evidence from lexical decision Utilizing Third-Order Hidden Markov Models for Emotional Talking Condition Recognition Speech Emotion Recognition Based on Multi-feature and Multi-lingual Fusion Speech emotion recognition using deep 1D & 2D CNN LSTM networks Multiview learning via deep discriminative canonical correlation analysis Speech Emotion Recognition Based on Robust Discriminative Sparse Regression Reordering Features with Weights Fusion in Multiclass and Multiple-Kernel Speech Emotion Recognition An Application of Support States from Speech Emotions in Consensus Building A bio-inspired emotion recognition system under real-life conditions Improving of Feature Selection in Speech Emotion Recognition Based-on Hybrid Evolutionary Algorithms Emotion Recognition: A Pattern Analysis Approach Speech Emotion Recognition System Using Gaussian Mixture Model and Improvement proposed via Boosted GMM Deep Speech: Scaling up end-to-end speech recognition Very deep multilingual convolutional neural networks for LVCSR DeepWalk: online learning of social representations Light Gated Recurrent Units for Speech Recognition Automatic emotion recognition in the wild using an ensemble of static and dynamic representations Research on Video Emotion Recognition Based on Attention Mechanism LSTM Model Partial least squares regression on grassmannian manifold for emotion recognition Modelling Temporal Information Using Discrete Fourier Transform for Video Classification Comparison of an Emotion Detection Technique on Posed and Spontaneous Datasets Video Emotion Recognition Using Local Enhanced Motion History Image and CNN-RNN Networks A Content-Based Retrieval Algorithm for Video Emotion Types Video Emotion Recognition with Concept Selection Video facial emotion recognition based on local enhanced motion history image and CNN-CTSLSTM networks The application of unascertained measure to the video emotion type recognition DeXpression: Deep Convolutional Neural Network for Expression Recognition
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