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.

This book captures the current challenges in automatic recognition of emotion in spontaneous speech and makes an effort to explain, elaborate, and propose possible solutions. Intelligent human–computer interaction (iHCI) systems thrive on several technologies like automatic speech recognition (ASR); speaker identification; language identification; image and video recognition; affect/mood/emotion analysis; and recognition, to name a few. Given the importance of spontaneity in any human–machine conversational speech, reliable recognition of emotion from naturally spoken spontaneous speech is crucial. While emotions, when explicitly demonstrated by an actor, are easy for a machine to recognize, the same is not true in the case of day-to-day, naturally spoken spontaneous speech. The book explores several reasons behind this, but one of the main reasons for this is that people, especially non-actors, do not explicitly demonstrate their emotion when they speak, thus making it difficult for machines to distinguish one emotion from another that is embedded in their spoken speech. This short book, based on some of authors’ previously published books, in the area of audio emotion analysis, identifies the practical challenges in analysing emotions in spontaneous speech and puts forward several possible solutions that can assist in robustly determining the emotions expressed in spontaneous speech.

The goal of this volume is to present a collection of papers illustrating state-of-the-art research on prosody and affective speech in French and in English. The volume is divided into two parts. The first part focusses on the sociolinguistic parameters that can influence the manifestation and the interpretation of affective speech in prosody. The second part relies on the way emotion recognition is implemented in synthesis systems and how machine applications can contribute to a better description of emotion(s).

This volume and its companion volume LNAI 4441 constitute a state-of-the-art survey in the field of speaker classification. Together they address such intriguing issues as how speaker characteristics are manifested in voice and speaking behavior. The nineteen contributions in this volume are organized into topical sections covering fundamentals, characteristics, applications, methods, and evaluation.

This book constitutes the refereed proceedings of the Second International Conference on Affective Computing and Intelligent Interaction, ACII 2007, held in Lisbon, Portugal, in September 2007. The 57 revised full papers and 4 revised short papers presented together with the extended abstracts of 33 poster papers were carefully reviewed and selected from 151 submissions. The papers are organized in topical sections on affective facial expression and recognition, affective body expression and recognition, affective speech processing, affective text and dialogue processing, recognising affect using physiological measures, computational models of emotion and theoretical foundations, affective databases, annotations, tools and languages, affective sound and music processing, affective interactions: systems and applications, as well as evaluating affective systems.

The 24 chapters in this book provides a deep overview of robotics and the application of AI and IoT in robotics. It contains the exploration of AI and IoT based intelligent automation in robotics. The various algorithms and frameworks for robotics based on AI and IoT are presented, analyzed, and discussed. This book also provides insights on application of robotics in education, healthcare, defense and many other fields which utilize IoT and AI. It also introduces the idea of smart cities using robotics.

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.

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 is an important reference to researchers and practitioners in the field of modern speech and emotional recognition. The purpose of the book is two different fold: first, to set the basic theoretical information of different classifications which is relevant to the human emotion recognition domain; second, to propose a practical orientation implementation of these methods to the emotion recognition system and also this book having the detailed description of different mathematical models which is used for recognition of human emotions.