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The recognition of the user's emotion-related state is one important step in making human-machine communication more natural. In this work, the focus is set on mono-modal systems with speech as only input channel. Current research has to shift from emotion portrayals to those states that actually appear in application-oriented scenarios. These states are mainly weak emotion-related states and mixtures of different states. The presented FAU Aibo Emotion Corpus is a major contribution in this area. It is a corpus of spontaneous, emotionally colored speech of children at the age of 10 to 13 years interacting with the Sony robot Aibo. 11 emotion-related states are labeled on the word level. Experiments are conducted on three subsets of the corpus on the word, the turn, and the intermediate chunk level. Best results have been obtained on the chunk level where a classwise averaged recognition rate of almost 70\% for the 4-class problem 'Anger', 'Emphatic', 'Neutral', and 'Motherese' has been achieved. Applying the proposed entropy based measure for the evaluation of decoders, the performance of the machine classifier on the word level is even slightly better than the one of the average human labeler. The presented set of features covers both acoustic and linguistic features. The linguistic features perform slightly worse than the acoustic features. An improvement can be achieved by combining both knowledge sources. The acoustic features are categorized into prosodic, spectral, and voice quality features. The energy and duration based prosodic features and the spectral MFCC features are the most relevant acoustic features in this scenario. Unigram models and bag-of-words features are the most relevant linguistic features.
Emotion pervades human life in general, and human communication in particular, and this sets information technology a challenge. Traditionally, IT has focused on allowing people to accomplish practical tasks efficiently, setting emotion to one side. That was acceptable when technology was a small part of life, but as technology and life become increasingly interwoven we can no longer ask people to suspend their emotional nature and habits when they interact with technology. The European Commission funded a series of related research projects on emotion and computing, culminating in the HUMAINE project which brought together leading academic researchers from the many related disciplines. This book grew out of that project, and its chapters are arranged according to its working areas: theories and models; signals to signs; data and databases; emotion in interaction; emotion in cognition and action; persuasion and communication; usability; and ethics and good practice. The fundamental aim of the book is to offer researchers an overview of the related areas, sufficient for them to do credible work on affective or emotion-oriented computing. The book serves as an academically sound introduction to the range of disciplines involved – technical, empirical and conceptual – and will be of value to researchers in the areas of artificial intelligence, psychology, cognition and user—machine interaction.
A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers. Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability. There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems. Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book Offers both foundations and advances on emotion recognition in a single volume Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains Inspires young researchers to prepare themselves for their own research Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.
This book reports on an outstanding thesis that has significantly advanced the state-of-the-art in the automated analysis and classification of speech and music. It defines several standard acoustic parameter sets and describes their implementation in a novel, open-source, audio analysis framework called openSMILE, which has been accepted and intensively used worldwide. The book offers extensive descriptions of key methods for the automatic classification of speech and music signals in real-life conditions and reports on the evaluation of the framework developed and the acoustic parameter sets that were selected. It is not only intended as a manual for openSMILE users, but also and primarily as a guide and source of inspiration for students and scientists involved in the design of speech and music analysis methods that can robustly handle real-life conditions.
This book constitutes the refereed proceedings of the 13th International Conference on Text, Speech and Dialogue, TSD 2010, held in Brno, Czech Republic, September 2010. The 71 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 144 submissions. The topics of the conference include, but are not limited to text corpora and tagging, transcription problems in spoken corpora, sense disambiguation, links between text and speech oriented systems, parsing issues, multi-lingual issues, information retrieval and information extraction, text/topic summarization, machine translation, semantic web, speech modeling, speech recognition, search in speech for IR and IE, text-to-speech synthesis, emotions and personality modeling, user modeling, knowledge representation in relation to dialogue systems, assistive technologies based on speech and dialogue, applied systems and software, facial animation, as well as visual speech synthesis.
Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms and Applications posits an approach for biosignal processing and classification using computational learning and intelligence, highlighting that the term biosignal refers to all kinds of signals that can be continuously measured and monitored in living beings. The book is composed of five relevant parts. Part One is an introduction to biosignals and Part Two describes the relevant techniques for biosignal processing, feature extraction and feature selection/dimensionality reduction. Part Three presents the fundamentals of computational learning (machine learning). Then, the main techniques of computational intelligence are described in Part Four. The authors focus primarily on the explanation of the most used methods in the last part of this book, which is the most extensive portion of the book. This part consists of a recapitulation of the newest applications and reviews in which these techniques have been successfully applied to the biosignals' domain, including EEG-based Brain-Computer Interfaces (BCI) focused on P300 and Imagined Speech, emotion recognition from voice and video, leukemia recognition, infant cry recognition, EEGbased ADHD identification among others. - Provides coverage of the fundamentals of signal processing, including sensing the heart, sending the brain, sensing human acoustic, and sensing other organs - Includes coverage biosignal pre-processing techniques such as filtering, artifiact removal, and feature extraction techniques such as Fourier transform, wavelet transform, and MFCC - Covers the latest techniques in machine learning and computational intelligence, including Supervised Learning, common classifiers, feature selection, dimensionality reduction, fuzzy logic, neural networks, Deep Learning, bio-inspired algorithms, and Hybrid Systems - Written by engineers to help engineers, computer scientists, researchers, and clinicians understand the technology and applications of computational learning to biosignal processing
Examines various speech technologies deployed in healthcare service robots to maximize the robot's ability to interpret user input. Demonstrates how robot anthropomorphic features and etiquette in behavior promotes user-positive emotions, acceptance of robots, and compliance with robot requests. Analyzes how multimodal medical-service robots and other cyber-physical systems can reduce mistakes and mishaps in the operating room. Evaluates various input methods for improving acceptance of robots in the older adult population. Presents case studies of cognitively and socially engaging robots in the long-term care setting for helping older adults with activities of daily living and in the pediatric setting for helping children with autism spectrum conditions and metabolic disorders. Speech and Automata in Health Care forges new ground by closely analyzing how three separate disciplines - speech technology, robotics, and medical/surgical/assistive care - intersect with one another, resulting in an innovative way of diagnosing and treating both juvenile and adult illnesses and conditions. This includes the use of speech-enabled robotics to help the elderly population cope with common problems associated with aging caused by the diminution in their sensory, auditory and motor capabilities. By examining the emerging nexus of speech, automata, and health care, the authors demonstrate the exciting potential of automata, both speech-driven and multimodal, to affect the healthcare delivery system so that it better meets the needs of the populations it serves. This book provides both empirical research findings and incisive literature reviews that demonstrate some of the more novel uses of speech-enabled and multimodal automata in the operating room, hospital ward, long-term care facility, and in the home. Studies backed by major universities, research institutes, and by EU-funded collaborative projects are debuted in this volume. This volume provides a wealth of timely material for industrial engineers, speech scientists, computational linguists, and for signal processing and intelligent systems design experts. Topics include: Spoken Interaction with Healthcare Robots Service Robot Feature Effects on Patient Acceptance/Emotional Response Designing Embodied and Virtual Agents for the Operating Room The Emerging Role of Robotics for Personal Health Management in the Older-Adult Population Why Input Methods for Robots that Serve the Older Adult Are Critical for Usability Socially and Cognitively Engaging Robots in the Long-Term Care Setting Voice-Enabled Assistive Robots for Managing Autism Spectrum Conditions ASR and TTS for Voice-Controlled Robot Interactions in Treating Children with Metabolic Disorders
Recent years have seen the rise of a remarkable partnership between the social and computational sciences on the phenomena of emotions. This book reports on the state-of-the-art in both social science theory and computational methods, and illustrates how these two fields, together, can both facilitate practical computer/robotic applications and illuminate human social processes.
This book constitutes the proceedings of the 5th International Conference on Nonlinear Speech Processing, NoLISP 2011, held in Las Palmas de Gran Canaria, Spain, in November 2011. The purpose of the workshop is to present and discuss new ideas, techniques and results related to alternative approaches in speech processing that may depart from the main stream. The 33 papers presented together with 2 keynote talks were carefully reviewed and selected for inclusion in this book. The topics of NOLISP 2011 were non-linear approximation and estimation; non-linear oscillators and predictors; higher-order statistics; independent component analysis; nearest neighbors; neural networks; decision trees; non-parametric models; dynamics of non-linear systems; fractal methods; chaos modeling; and non-linear differential equations.
This book addresses the automatic analysis of speech disorders resulting from a clinical condition (Parkinson's disease and hearing loss) or the natural aging process. For Parkinson's disease, the progression of speech symptoms is evaluated by considering speech recordings captured in the short-term (4 months) and long-term (5 years). Machine learning methods are used to perform three tasks: (1) automatic classification of patients vs. healthy speakers. (2) regression analysis to predict the dysarthria level and neurological state. (3) speaker embeddings to analyze the progression of the speech symptoms over time. For hearing loss, automatic acoustic analysis is performed to evaluate whether the duration and onset of deafness (before or after speech acquisition) influence the speech production of cochlear implant users. Additionally, articulation, prosody, and phonemic analyses show that cochlear implant users present altered speech production even after hearing rehabilitation.