Don't Believe The Hype!:White Lies of Conversational User Interface Creation Tools

The 2nd International Conference on Conversational User Interfaces (CUI 2020), Bilbao, Spain, 23-24 July 2020Following the initial hype and high expectations of conversational user interfaces (CUIs), a number of creation tools have emerged to simplify development of these complex systems. These have the potential to democratise and expand application development to those without programming skills. However, while such tools allow end-user developers to build language understanding and dialog management capability into a CUI application, actually fulfilling or executing an action still requires programmatic API integration. In this paper, we look at how CUI builders that claim to be ``no code required'' struggle to yield more than toy examples, with an aim to provoke the community to develop better tools for CUI creation.Trinity College Dublin (TCD

Facilitating Preference Revision through a Spoken Dialogue System

We present the design of a spoken dialogue system to provide feedback to users of an autonomous system which can learn different patterns associated with user actions. Our speech interface allows users to verbally refine these patterns, giving the system his/her feedback about the accuracy of the actions learnt.We focus on improving the naturalness of user interventions, using a stochastic language model and a rule-based language understanding module. The development of a state-based di- alogue manager which decides how to conduct each dialogue, together with the storage of contextual information of previous dialogue turns, allows the user to speak to the system in a highly natural way

The potential of app-based mental health assessment using machine learning-based voice analysis

In a world where accessing mental health and wellbeing services remains challenging due to resource constraints and societal stigmas, the pandemic has intensified the need for innovative access methods. Traditional face-to-face service avenues have become strained, highlighting the importance of digital interventions in mental health care. This research details the journey towards use of app-based voice biomarker assessments for those seeking mental health support in increasingly digital landscapes. The study, spanning 12 weeks, investigated the effectiveness of using voice biomarkers for mental health assessment among young adults aged 16-24. An app-based platform facilitated weekly self-assessments at the participants' discretion and incorporated machine learning to analyse voice data for signs of mental health struggles.Participants engaged in weekly sessions, prompted by a question about their day to capture voice samples, with machine learning algorithms providing the analytical backbone. These were assessed alongside established mental health questionnaires to identify markers of depression and anxiety. This blend of technology and traditional assessments aimed to uncover insights into the participants' mental health.Our analysis reveals that while demographic factors marginally influence model accuracy, the severity of mental health conditions significantly impacts detection capabilities. This nuanced understanding underscores the potential of digital interventions in providing more accessible, personalised mental health support.In the broader context of the post-pandemic push towards digital mental health solutions, our findings contribute knowledge towards technological innovations aligned with the goal of services that are not only effective, but also accessible. The research shows the potential and challenges associated with voice biomarker technology in revolutionising mental health assessment and enhancing care through digital means.<br/

Community level digital mental health interventions:A policy and practice brief

The prevalence of mental ill-health is increasing worldwide and brings adverse consequences at both the individual and societal level. Treatments and interventions for the symptoms that represent mental health conditions may target biological, behavioural and cognitive factors. Traditionally, treatments have included psychotropic medication, and/or psychological therapies which are delivered on a one to one or group basis. Both have a high economic cost, and efficacy varies. In addition, help seeking behaviour is impacted by stigma, symptom recognition &amp; understanding, and a host of factors associated with the disorders themselves, such as avoidance behaviour. The delivery of face-to-face interventions for those who are most marginalised and most at risk from mental ill-health, can also be impacted by barriers, such as knowledge of the services available and time, connectivity or travel constraints. The research presented here is co-produced with service providers, end users and academic experts across the disciplines of psychology, business, medicine, healthcare, interaction design and computer science. This briefing is based on the findings from our research programme on a community level digital mental health intervention.<br/