Distant microphone speech recognition systems that operate with humanlike robustness remain a distant goal. The key difficulty is that operating in everyday listening conditions entails processing a speech signal that is reverberantly mixed into a noise background composed of multiple competing sound sources. This paper describes a recent speech recognition evaluation that was designed to bring together researchers from multiple communities in order to foster novel approaches to this problem. The task was to identify keywords from sentences reverberantly mixed into audio backgrounds binaurally-recorded in a busy domestic environment. The challenge was designed to model the essential difficulties of multisource environment problem while remaining on a scale that would make it accessible to a wide audience. Compared to previous ASR evaluation a particular novelty of the task is that the utterances to be recognised were provided in a continuous audio background rather than as pre-segmented utterances thus allowing a range of background modelling techniques to be employed. The challenge attracted thirteen submissions. This paper describes the challenge problem, provides an overview of the systems that were entered and provides a comparison alongside both a baseline recognition system and human performance. The paper discusses insights gained from the challenge and lessons learnt for the design of future such evaluations.

Background Individuals living with long-term physical health conditions frequently experience co-occurring mental health problems. This comorbidity has a significant impact on an individual’s levels of emotional distress, health outcomes, and associated health care utilization. As health care services struggle to meet demand and care increasingly moves to the community, digital tools are being promoted to support patients to self-manage their health. One such technology is the autonomous virtual agent (chatbot, conversational agent), which uses artificial intelligence (AI) to process the user’s written or spoken natural language and then to select or construct the corresponding appropriate responses. Objective This study aimed to co-design the content, functionality, and interface modalities of an autonomous virtual agent to support self-management for patients with an exemplar long-term condition (LTC; chronic pulmonary obstructive disease [COPD]) and then to assess the acceptability and system content. Methods We conducted 2 co-design workshops and a proof-of-concept implementation of an autonomous virtual agent with natural language processing capabilities. This implementation formed the basis for video-based scenario testing of acceptability with adults with a diagnosis of COPD and health professionals involved in their care. Results Adults (n=6) with a diagnosis of COPD and health professionals (n=5) specified 4 priority self-management scenarios for which they would like to receive support: at the time of diagnosis ( information provision ), during acute exacerbations ( crisis support ), during periods of low mood ( emotional support ), and for general self-management ( motivation ). From the scenario testing, 12 additional adults with COPD felt the system to be both acceptable and engaging, particularly with regard to internet-of-things capabilities. They felt the system would be particularly useful for individuals living alone. Conclusions Patients did not explicitly separate mental and physical health needs, although the content they developed for the virtual agent had a clear psychological approach. Supported self-management delivered via an autonomous virtual agent was acceptable to the participants. A co-design process has allowed the research team to identify key design principles, content, and functionality to underpin an autonomous agent for delivering self-management support to older adults living with COPD and potentially other LTCs.

This study tested several aspects of restraint theory using female college students as subjects. In a 2 X 2 X 2 between-subjects design, the ice cream consumption of overweight and normal-weight restrained and unrestrained eaters was measured during a taste test. Prior to the taste test half of the subjects received a milkshake preload. The results revealed significant Restraint X Preload, Weight X Preload, and Restraint X Preload interactions. As previous research has found and restraint theory has predicted, unrestrained eaters consumed less after the preload than without it, whereas restrained eaters ate more. Contrary to the theory's prediction, overweight individuals ate less after the preload than without it, whereas normalweight people ate slightly more. A second finding inconsistent with the theory was that among overweight people, unrestrained eaters ate more than restrained eaters, whereas among normal-weight people, the reverse was true. The significant Weight X Preload and Restraint X Preload interactions were interpreted as possibly reflecting psychometric problems in the restraint scale. Numerically equivalent scores may indicate less "true" restraint in overweight than in normalweight individuals.Psychological theories concerning obesity have attempted to describe and explain eating patterns that characterize overweight persons. Schachter (1971) hypothesized that external cues, such as the sight, smell, and taste of food, trigger eating among overweight individuals, whereas internal cues, such as gastric contractions, trigger eating among normal-weight people. This hypothesis has not received strong confirmation (Leon & Roth, 1977;Rodin, 1981;Wooley&Wooley, 1975). An alternative model presented by Nisbett (1972) proposed that everyone has a biologically determined ideal weight or set point. However, because of social pressure to look slim, many people keep their weight below this biological ideal. According to this model, people may be overweight by statistically normative standards, whereas they are underweight by biological criteria. Nisbett proposed that many obese individuals fit this description and, as a result, they develop eating patterns that resemble those of starving organisms.

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Neurogenerative disorders, like dementia, can affect a person's speech, language and as a consequence, conversational interaction capabilities. A recent study, aimed at improving dementia detection accuracy, investigated the use of conversation analysis (CA) of interviews between patients and neurologists as a means to differentiate between patients with progressive neurodegenerative memory disorder (ND) and those with (non-progressive) functional memory disorders (FMD). However, manual CA is expensive and difficult to scale up for routine clinical use. In this paper, we present an automatic classification using an intelligent virtual agent (IVA). In particular, using two parallel corpora of respectively neurologist-and IVA-led interactions, we show that using acoustic, lexical and CA-inspired features enables ND/FMD classification rates of 90.0% for the neurologist-patient conversations, and an encouraging 90.9% for the IVApatient conversations. Analysis of the significance of individual features show that some differences exist between the IVA and human-led conversations for example in average turn length of patients.