Emotional recognition in computing

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University 8/4/2010.Emotions are fundamental to human lives and decision-making. Understanding and expression of emotional feeling between people forms an intricate web. This complex interactional phenomena, is a hot topic for research, as new techniques such as brain imaging give us insights about how emotions are tied to human functions. Communication of emotions is mixed with communication of other types of information (such as factual details) and emotions can be consciously or unconsciously displayed. Affective computer systems, using sensors for emotion recognition and able to make emotive responses are under development. The increased potential for emotional interaction with products and services, in many domains, is generating much interest. Emotionally enhanced systems have potential to improve human computer interaction and so to improve how systems are used and what they can deliver. They may also have adverse implications such as creating systems capable of emotional manipulation of users. Affective systems are in their infancy and lack human complexity and capability. This makes it difficult to assess whether human interaction with such systems will actually prove beneficial or desirable to users. By using experimental design, a Wizard of Oz methodology and a game that appeared to respond to the user’s emotional signals with human-like capability, I tested user experience and reactions to a system that appeared affective. To assess users’ behaviour, I developed a novel affective behaviour coding system called ‘affectemes’. I found significant gains in user satisfaction and performance when using an affective system. Those believing the system responded to emotional signals blinked more frequently. If the machine failed to respond to their emotional signals, they increased their efforts to convey emotion, which might be an attempt to ‘repair’ the interaction. This work highlights how very complex and difficult it is to design and evaluate affective systems. I identify many issues for future work, including the unconscious nature of emotions and how they are recognised and displayed with affective systems; issues about the power of emotionally interactive systems and their evaluation; and critical ethical issues. These are important considerations for future design of systems that use emotion recognition in computing.EPSRC project grant (R81374/01

Data-Driven Policy Optimisation for Multi-Domain Task-Oriented Dialogue

Recent developments in machine learning along with a general shift in the public attitude towards digital personal assistants has opened new frontiers for conversational systems. Nevertheless, building data-driven multi-domain conversational agents that act optimally given a dialogue context is an open challenge. The first step towards that goal is developing an efficient way of learning a dialogue policy in new domains. Secondly, it is important to have the ability to collect and utilise human-human conversational data to bootstrap an agent's knowledge. The work presented in this thesis demonstrates how a neural dialogue manager fine-tuned with reinforcement learning presents a viable approach for learning a dialogue policy efficiently and across many domains. The thesis starts by introducing a dialogue management module that learns through interactions to act optimally given a current context of a conversation. The current shift towards neural, parameter-rich systems does not fully address the problem of error noise coming from speech recognition or natural language understanding components. A Bayesian approach is therefore proposed to learn more robust and effective policy management in direct interactions without any prior data. By putting a distribution over model weights, the learning agent is less prone to overfit to particular dialogue realizations and a more efficient exploration policy can be therefore employed. The results show that deep reinforcement learning performs on par with non-parametric models even in a low data regime while significantly reducing the computational complexity compared with the previous state-of-the-art. The deployment of a dialogue manager without any pre-training on human conversations is not a viable option from an industry perspective. However, the progress in building statistical systems, particularly dialogue managers, is hindered by the scale of data available. To address this fundamental obstacle, a novel data-collection pipeline entirely based on crowdsourcing without the need for hiring professional annotators is introduced. The validation of the approach results in the collection of the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully labeled collection of human-human written conversations spanning over multiple domains and topics. The proposed dataset creates a set of new benchmarks (belief tracking, policy optimisation, and response generation) significantly raising the complexity of analysed dialogues. The collected dataset serves as a foundation for a novel reinforcement learning (RL)-based approach for training a multi-domain dialogue manager. A Multi-Action and Slot Dialogue Agent (MASDA) is proposed to combat some limitations: 1) handling complex multi-domain dialogues with multiple concurrent actions present in a single turn; and 2) lack of interpretability, which consequently impedes the use of intermediate signals (e.g., dialogue turn annotations) if such signals are available. MASDA explicitly models system acts and slots using intermediate signals, resulting in an improved task-based end-to-end framework. The model can also select concurrent actions in a single turn, thus enriching the representation of the generated responses. The proposed framework allows for RL training of dialogue task completion metrics when dealing with concurrent actions. The results demonstrate the advantages of both 1) handling concurrent actions and 2) exploiting intermediate signals: MASDA outperforms previous end-to-end frameworks while also offering improved scalability.EPSR

Designing Personas for Expressive Robots: Personality in the New Breed of Moving, Speaking, and Colorful Social Home Robots

Imbuing robots with personality has been shown to be an effective design approach in HRI, promoting user trust and acceptance. We explore personality design in a non-anthropomorphic voice-assisted home robot. Our design approach developed three distinct robot personas: Butler, Buddy, and Sidekick, intended to differ in proactivity and emotional impact. Persona differences were signaled to users by a combination of humanoid (speech, intonation), and indirect cues (colors and movement). We use Big Five personality theory to evaluate perceived differences between personas in an exploratory Wizard of Oz study. Participants were largely able to recognize underlying personality traits expressed through these cue combinations in ways that were consistent with our design goals. The proactive Buddy persona was judged as more Extravert than the more passive Sidekick persona, and the Butler persona was perceived as more Conscientious and less Neurotic than either Buddy or Butler personas. Users also had clear preferences between different personas; they wanted robots that mimicked but accentuated their own personality. Results suggest that future designs might exploit abstract cues to signal personality traits