280,840 research outputs found
Machine Understanding of Human Behavior
A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior
Spoken affect classification : algorithms and experimental implementation : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand
Machine-based emotional intelligence is a requirement for natural interaction between humans and computer interfaces and a basic level of accurate emotion perception is needed for computer systems to respond adequately to human emotion. Humans convey emotional information both intentionally and unintentionally via speech patterns. These vocal patterns are perceived and understood by listeners during conversation. This research aims to improve the automatic perception of vocal emotion in two ways. First, we compare two emotional speech data sources: natural, spontaneous emotional speech and acted or portrayed emotional speech. This comparison demonstrates the advantages and disadvantages of both acquisition methods and how these methods affect the end application of vocal emotion recognition. Second, we look at two classification methods which have gone unexplored in this field: stacked generalisation and unweighted vote. We show how these techniques can yield an improvement over traditional classification methods
Artificial consciousness and the consciousness-attention dissociation
Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systemsâthese will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness
Emotion Recognition from Acted and Spontaneous Speech
DizertaÄnĂ prĂĄce se zabĂœvĂĄ rozpoznĂĄnĂm emoÄnĂho stavu mluvÄĂch z ĆeÄovĂ©ho signĂĄlu. PrĂĄce je rozdÄlena do dvou hlavnĂch ÄastĂ, prvnĂ ÄĂĄst popisuju navrĆŸenĂ© metody pro rozpoznĂĄnĂ emoÄnĂho stavu z hranĂœch databĂĄzĂ. V rĂĄmci tĂ©to ÄĂĄsti jsou pĆedstaveny vĂœsledky rozpoznĂĄnĂ pouĆŸitĂm dvou rĆŻznĂœch databĂĄzĂ s rĆŻznĂœmi jazyky. HlavnĂmi pĆĂnosy tĂ©to ÄĂĄsti je detailnĂ analĂœza rozsĂĄhlĂ© ĆĄkĂĄly rĆŻznĂœch pĆĂznakĆŻ zĂskanĂœch z ĆeÄovĂ©ho signĂĄlu, nĂĄvrh novĂœch klasifikaÄnĂch architektur jako je napĆĂklad âemoÄnĂ pĂĄrovĂĄnĂâ a nĂĄvrh novĂ© metody pro mapovĂĄnĂ diskrĂ©tnĂch emoÄnĂch stavĆŻ do dvou dimenzionĂĄlnĂho prostoru. DruhĂĄ ÄĂĄst se zabĂœvĂĄ rozpoznĂĄnĂm emoÄnĂch stavĆŻ z databĂĄze spontĂĄnnĂ ĆeÄi, kterĂĄ byla zĂskĂĄna ze zĂĄznamĆŻ hovorĆŻ z reĂĄlnĂœch call center. Poznatky z analĂœzy a nĂĄvrhu metod rozpoznĂĄnĂ z hranĂ© ĆeÄi byly vyuĆŸity pro nĂĄvrh novĂ©ho systĂ©mu pro rozpoznĂĄnĂ sedmi spontĂĄnnĂch emoÄnĂch stavĆŻ. JĂĄdrem navrĆŸenĂ©ho pĆĂstupu je komplexnĂ klasifikaÄnĂ architektura zaloĆŸena na fĂșzi rĆŻznĂœch systĂ©mĆŻ. PrĂĄce se dĂĄle zabĂœvĂĄ vlivem emoÄnĂho stavu mluvÄĂho na ĂșspÄĆĄnosti rozpoznĂĄnĂ pohlavĂ a nĂĄvrhem systĂ©mu pro automatickou detekci ĂșspÄĆĄnĂœch hovorĆŻ v call centrech na zĂĄkladÄ analĂœzy parametrĆŻ dialogu mezi ĂșÄastnĂky telefonnĂch hovorĆŻ.Doctoral thesis deals with emotion recognition from speech signals. The thesis is divided into two main parts; the first part describes proposed approaches for emotion recognition using two different multilingual databases of acted emotional speech. The main contributions of this part are detailed analysis of a big set of acoustic features, new classification schemes for vocal emotion recognition such as âemotion couplingâ and new method for mapping discrete emotions into two-dimensional space. The second part of this thesis is devoted to emotion recognition using multilingual databases of spontaneous emotional speech, which is based on telephone records obtained from real call centers. The knowledge gained from experiments with emotion recognition from acted speech was exploited to design a new approach for classifying seven emotional states. The core of the proposed approach is a complex classification architecture based on the fusion of different systems. The thesis also examines the influence of speakerâs emotional state on gender recognition performance and proposes system for automatic identification of successful phone calls in call center by means of dialogue features.
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