Download or read book Robust Feature Learning for Acoustic Emotion Recognition written by Rui Xia and published by . This book was released on 2015 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: With increasing needs and developments of human-computer interaction systems, building robust systems to let computers understand humans' mood has become one of the essential components. Automatic emotion recognition/detection is necessary for machines to explore humans' emotional expressions. This dissertation focuses on learning robust features for building acoustic emotion recognition systems.We first investigate extracting features from frame-level based features. We adopt the ivector space modeling method to extract high-level features. Furthermore, based on i-vector space modeling, we develop the novel framework to generate multiple i-vector feature sets associated with emotion classes. The i-vector feature sets yield competitive performance with supra-segmental level based features, and the performance improves using a decision combination of the frame level based system and supra-segmental level based system. Second, based on supra-segmental level features, we apply deep learning techniques to extract high-level emotional feature representations. We propose a framework based on the neural network structure to project the original feature space into two different feature representations, and extract the one with more emotional cues as new features. In addition, we propose to model genders individually in the neural network structure in order to alleviate the gender variability to improve emotion recognition performance. Furthermore, we utilize multi-task learning by considering continuous dimensional information to improve categorical emotion recognition. Finally, we develop a novel framework using Deep Belief Network (DBN) in the paradigm of i-vector space modeling approach. This framework can combine the advantages of the two approaches. The DBN is discriminatively trained using reference labels automatically generated by the universal background model (UBM) and Gaussian Mixture Models (GMMs). The i-vector feature set obtained from this proposed method outperforms the traditional i-vector extracting framework. We believe a robust feature set is very important in emotion recognition systems. This dissertation will contribute to a general approach to learn a high-level rich emotional feature representation, which can advance the performance of current emotion recognition systems.
Download or read book New Era for Robust Speech Recognition written by Shinji Watanabe and published by Springer. This book was released on 2017-10-30 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
Download or read book Robust Emotion Recognition using Spectral and Prosodic Features written by K. Sreenivasa Rao and published by Springer Science & Business Media. This book was released on 2013-01-13 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this brief, the authors discuss recently explored spectral (sub-segmental and pitch synchronous) and prosodic (global and local features at word and syllable levels in different parts of the utterance) features for discerning emotions in a robust manner. The authors also delve into the complementary evidences obtained from excitation source, vocal tract system and prosodic features for the purpose of enhancing emotion recognition performance. Features based on speaking rate characteristics are explored with the help of multi-stage and hybrid models for further improving emotion recognition performance. Proposed spectral and prosodic features are evaluated on real life emotional speech corpus.
Download or read book Emotion and Stress Recognition Related Sensors and Machine Learning Technologies written by Kyandoghere Kyamakya and published by MDPI. This book was released on 2021-09-01 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective. This book, emerging from the Special Issue of the Sensors journal on “Emotion and Stress Recognition Related Sensors and Machine Learning Technologies” emerges as a result of the crucial need for massive deployment of intelligent sociotechnical systems. Such technologies are being applied in assistive systems in different domains and parts of the world to address challenges that could not be addressed without the advances made in these technologies.
Download or read book Music Emotion Recognition written by Yi-Hsuan Yang and published by CRC Press. This book was released on 2011-02-22 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a complete review of existing work in music emotion developed in psychology and engineering, Music Emotion Recognition explains how to account for the subjective nature of emotion perception in the development of automatic music emotion recognition (MER) systems. Among the first publications dedicated to automatic MER, it begins with
Download or read book Intelligent Robotics written by Zhiwen Yu and published by Springer Nature. This book was released on 2023-02-17 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes selected papers presented during the Third China Annual Intelligent Robotics Conference, CCF CIRAC 2022, held in Xi' an, China, in December 2022. The 35 papers presented were thoroughly reviewed and selected from the 120 qualified submissions. They are organized in the following topical sections: robot safety; intelligent robot sensing; autonomous robot navigation; artificial intelligence and cloud robot; unmanned cluster collaboration; natural human-computer interaction; other robot-related technologies.
Download or read book Improving Robustness of Emotional Speech Detection System written by Tauhidur Rahman and published by . This book was released on 2012 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical deployment of emotional speech detection systems requires robust algorithms that can compensate the variability observed in uncontrolled, realistic conditions. An emotion classifier should deal with mixed subtle emotions, speaker variability, different recording settings and language mismatches. This study proposes robust solutions at the feature and model level for speech emotion detection systems. First, we study the discriminative power of acoustic features in the valence dimension (positive versus negative). This is a major challenge, given the lack of discrimination of acoustic features in this emotional dimension. A systematic study is presented to select the most relevant speech features associated with valence. Then, a front end unsupervised feature adaptation scheme is proposed. The scheme iteratively normalizes the features to minimize speaker variability, while preserving the emotional discrimination. Finally, the study explores robust approaches for the emotional models. The proposed solutions include the use of synthetic speech as a neutral reference to contrast emotional speech. A complementary solution is also proposed based on co-adaptation. The approach adapts the machine learning algorithms to minimize mismatches between training and testing conditions. The results demonstrate the benefits of the proposed work.
Download or read book Artificial Intelligence and Cybersecurity written by Ishaani Priyadarshini and published by CRC Press. This book was released on 2022-02-04 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence and cybersecurity are two emerging fields that have made phenomenal contributions toward technological advancement. As cyber-attacks increase, there is a need to identify threats and thwart attacks. This book incorporates recent developments that artificial intelligence brings to the cybersecurity world. Artificial Intelligence and Cybersecurity: Advances and Innovations provides advanced system implementation for Smart Cities using artificial intelligence. It addresses the complete functional framework workflow and explores basic and high-level concepts. The book is based on the latest technologies covering major challenges, issues and advances, and discusses intelligent data management and automated systems. This edited book provides a premier interdisciplinary platform for researchers, practitioners and educators. It presents and discusses the most recent innovations, trends and concerns as well as practical challenges and solutions adopted in the fields of artificial intelligence and cybersecurity.
Download or read book The Oxford Handbook of Affective Computing written by Rafael A. Calvo and published by Oxford Library of Psychology. This book was released on 2015 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The Oxford Handbook of Affective Computing is a definitive reference in the burgeoning field of affective computing (AC), a multidisciplinary field encompassing computer science, engineering, psychology, education, neuroscience, and other disciplines. AC research explores how affective factors influence interactions between humans and technology, how affect sensing and affect generation techniques can inform our understanding of human affect, and on the design, implementation, and evaluation of systems involving affect at their core. The volume features 41 chapters and is divided into five sections: history and theory, detection, generation, methodologies, and applications. Section 1 begins with the making of AC and a historical review of the science of emotion. The following chapters discuss the theoretical underpinnings of AC from an interdisciplinary viewpoint. Section 2 examines affect detection or recognition, a commonly investigated area. Section 3 focuses on aspects of affect generation, including the synthesis of emotion and its expression via facial features, speech, postures, and gestures. Cultural issues are also discussed. Section 4 focuses on methodological issues in AC research, including data collection techniques, multimodal affect databases, formats for the representation of emotion, crowdsourcing techniques, machine learning approaches, affect elicitation techniques, useful AC tools, and ethical issues. Finally, Section 5 highlights applications of AC in such domains as formal and informal learning, games, robotics, virtual reality, autism research, health care, cyberpsychology, music, deception, reflective writing, and cyberpsychology. This compendium will prove suitable for use as a textbook and serve as a valuable resource for everyone with an interest in AC."--
Download or read book Artificial Intelligence and Natural Language written by Andrey Filchenkov and published by Springer. This book was released on 2017-11-28 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th Conference on Artificial Intelligence and Natural Language, AINL 2017, held in St. Petersburg, Russia, in September 2017. The 13 revised full papers, 4 revised short papers papers were carefully reviewed and selected from 35 submissions. The papers are organized in topical sections on social interaction analysis, speech processing, information extraction, Web-scale data processing, computation morphology and word embedding, machine learning. The volume also contains 6 papers participating in the Russian paraphrase detection shared task.
Download or read book Emotion and Stress Recognition Related Sensors and Machine Learning Technologies written by Kyandoghere Kyamakya and published by . This book was released on 2021 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective.
Download or read book Acoustic Modeling for Emotion Recognition written by Koteswara Rao Anne and published by . This book was released on 2015 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications - gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.
Download or read book Computer Vision ECCV 2016 Workshops written by Gang Hua and published by Springer. This book was released on 2016-11-23 with total page 938 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. 27 workshops from 44 workshops proposals were selected for inclusion in the proceedings. These address the following themes: Datasets and Performance Analysis in Early Vision; Visual Analysis of Sketches; Biological and Artificial Vision; Brave New Ideas for Motion Representations; Joint ImageNet and MS COCO Visual Recognition Challenge; Geometry Meets Deep Learning; Action and Anticipation for Visual Learning; Computer Vision for Road Scene Understanding and Autonomous Driving; Challenge on Automatic Personality Analysis; BioImage Computing; Benchmarking Multi-Target Tracking: MOTChallenge; Assistive Computer Vision and Robotics; Transferring and Adapting Source Knowledge in Computer Vision; Recovering 6D Object Pose; Robust Reading; 3D Face Alignment in the Wild and Challenge; Egocentric Perception, Interaction and Computing; Local Features: State of the Art, Open Problems and Performance Evaluation; Crowd Understanding; Video Segmentation; The Visual Object Tracking Challenge Workshop; Web-scale Vision and Social Media; Computer Vision for Audio-visual Media; Computer VISion for ART Analysis; Virtual/Augmented Reality for Visual Artificial Intelligence; Joint Workshop on Storytelling with Images and Videos and Large Scale Movie Description and Understanding Challenge.
Download or read book Feature Design for Robust Speech Recognition written by Shuo-Yiin Chang and published by . This book was released on 2016 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: As has been extensively shown, acoustic features for speech recognition can be nurtured from training data using neural networks (DNN) with multiple hidden layers. Although a large body of research has shown these learned features are superior to standard front- ends, this superiority is usually demonstrated when the data used to learn the features is very similar to the data used to test recognition performance. However, realistic environments cover many unanticipated types of novel inputs including noise, channel distortion, reverberation, accented speech, speaking rate variation, overlapped speech, etc. A quantitative analysis using bootstrap sampling shows that these trained features are easily specialized to training data and corrupted in mismatched scenarios. Gabor filtered spectrograms, on the other hand, are generated from spectro-temporal filters to model natural human auditory processing, which can be instrumental in improving generalization to unanticipated deviations from what was seen in training. In this thesis, I used Gabor filtering as feature processing or a convolutional kernel in neural networks where the former used filter outputs as DNN inputs while the latter used filter coefficients and structures to initialize a convolutional neural network (CNN). Experiments show that the proposed features perform better than other noise-robust features that I have tried on several noisy corpora. In addition, I demonstrate that inclusion of Gabor filters with lower or higher temporal modulations could be used to correlate better with human perception of slow or rapid speech. Finally, I report on the analysis of human cortical signals to demonstrate the relative robustness of these signals to the mixed signal phenomenon in contrast to a DNN-based ASR system. With a number of example tasks in the thesis, I conclude that designed feature is useful for greater robustness than just relying on DNN or CNN.
Download or read book Progress in Pattern Recognition Image Analysis Computer Vision and Applications written by Eduardo Bayro-Corrochano and published by Springer. This book was released on 2014-10-23 with total page 1071 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014, held in Puerto Vallarta, Jalisco, Mexico, in November 2014. The 115 papers presented were carefully reviewed and selected from 160 submissions. The papers are organized in topical sections on image coding, processing and analysis; segmentation, analysis of shape and texture; analysis of signal, speech and language; document processing and recognition; feature extraction, clustering and classification; pattern recognition and machine learning; neural networks for pattern recognition; computer vision and robot vision; video segmentation and tracking.
Download or read book Statistical Machine Learning for Human Behaviour Analysis written by Thomas Moeslund and published by MDPI. This book was released on 2020-06-17 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.
Download or read book Towards Adaptive Spoken Dialog Systems written by Alexander Schmitt and published by Springer Science & Business Media. This book was released on 2012-09-19 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recognition of negative dialog patterns in Spoken Dialog Systems (SDS). The presented stochastic methods allow a flexible, portable and accurate use. Beginning with the foundations of machine learning and pattern recognition, this monograph examines how frequently users show negative emotions in spoken dialog systems and develop novel approaches to speech-based emotion recognition using hybrid approach to model emotions. The authors make use of statistical methods based on acoustic, linguistic and contextual features to examine the relationship between the interaction flow and the occurrence of emotions using non-acted recordings several thousand real users from commercial and non-commercial SDS. Additionally, the authors present novel statistical methods that spot problems within a dialog based on interaction patterns. The approaches enable future SDS to offer more natural and robust interactions. This work provides insights, lessons and inspiration for future research and development, not only for spoken dialog systems, but for data-driven approaches to human-machine interaction in general.
