The autonomic nervous system.

Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever- evolving transportation system. Anticipating the public acceptance and adoption of these technologies is important. A recent internet-based survey was conducted polling 347 Austinites to understand their opinions on smart-car technologies and strategies. Ordered-probit and other model results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7,253) appears to be much higher than that for adding partial (Level 3) automation ($3,300) to their current vehicles. This study estimates the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding such automations and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, living in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.

Assessing Public Opinions of and Interest in New Vehicle Technologies: An Austin Perspective

Studies in Health Technology and Informatics

Omnidirectional or 360° video is increasingly being used, mostly due to the latest advancements in immersive Virtual Reality (VR) technology. However, its wide adoption is hindered by the higher bandwidth and lower latency requirements than associated with traditional video content delivery. Diverse researchers propose and design solutions that help support an immersive visual experience of 360° video, primarily when delivered over a dynamic network environment. This paper presents the state-of-the-art on adaptive 360° video delivery solutions considering end-to-end video streaming in general and then specifically of 360° video delivery. Current and emerging solutions for adaptive 360° video streaming, including viewport-independent, viewport-dependent, and tile-based schemes are presented. Next, solutions for network-assisted unicast and multicast streaming of 360° video content are discussed. Different research challenges for both on-demand and live 360° video streaming are also analyzed. Several proposed standards and technologies and top international research projects are then presented. We demonstrate the ongoing standardization efforts for 360° media services that ensure interoperability and immersive media deployment on a massive scale. Finally, the paper concludes with a discussion about future research opportunities enabled by 360° video.

A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities

Prior research has shown that articulatory information, if extracted properly from the speech signal, can improve the performance of automatic speech recognition systems. However, such information is not readily available in the signal. The challenge posed by the estimation of articulatory information from speech acoustics has led to a new line of research known as “acoustic-to-articulatory inversion” or “speech-inversion.” While most of the research in this area has focused on estimating articulatory information more accurately, few have explored ways to apply this information in speech recognition tasks. In this paper, we first estimated articulatory information in the form of vocal tract constriction variables (abbreviated as TVs) from the Aurora-2 speech corpus using a neural network based speech-inversion model. Word recognition tasks were then performed for both noisy and clean speech using articulatory information in conjunction with traditional acoustic features. Our results indicate that incorporating TVs can significantly improve word recognition rates when used in conjunction with traditional acoustic features.

Articulatory Information for Noise Robust Speech Recognition

The availability of affordable head-mounted displays has resulted in renewed interest in augmented reality (AR) and virtual reality (VR). This in turn has led to research into users' perceptions of quality in AR and VR environments. In this paper, the authors report the results of an experimental study that compared the user quality of experience (QoE) of an interactive and immersive speech and language assessment implemented in both AR and VR. To the best of the authors' knowledge, this is the first work that compares user QoE of VR and AR applications, in particular with a focus on applications in the speech and language domain. Subjective and objective data was captured to assess a user's QoE. Objective data, such as heart rate and electrodermal activity (EDA) was collected using consumer electronic devices such as the Fitbit and the PIP biosensor. Subjective data was captured using a user's self-reported ratings in a post-test questionnaire. The findings of this study demonstrate similar QoE ratings for both the AR and VR environments. The findings also suggest that users acclimatized to the AR environment more quickly than the VR environment.

A QoE evaluation of immersive augmented and virtual reality speech & language assessment applications

The availability of affordable head-mounted display technology has facilitated new, potentially more immersive, interactive multimedia experiences. These technologies were traditionally focused on entertainment; however, academia and industry are now exploring applications in other domains such as health, learning and training. Key to the success of these new multimedia experiences is the understanding of a user's perceived quality of experience (QoE). Subjective user ratings have been the primary mechanism to capture insights into a user's experience. Such ratings have generally been captured post experience and reflected using a mean opinion score (MOS). However, user perception is multifactorial and subjective ratings alone do not express the true measure of an experience. As a result, recent efforts to capture QoE have included exploring the use of implicit metrics (e.g., physiological measures). This article presents the results of an experimental QoE evaluation and comparison of immersive applications delivered across three multimedia platforms. The platforms compared were augmented reality, tablet and virtual reality. The QoE methodology employed considered explicit (post-test questionnaire) and implicit (heart rate and electrodermal activity) assessment methods. The results indicate comparatively higher levels of QoE for users of the augmented reality and tablet platforms.

A Physiology-Based QoE Comparison of Interactive Augmented Reality, Virtual Reality and Tablet-Based Applications

The1 key aim of various assistive technology (AT) systems is to augment an individual's functioning whilst supporting an enhanced quality of life (QoL). In recent times, we have seen the emergence of Virtual Reality (VR) based assistive technology systems made possible by the availability of commercially available Head Mounted Displays (HMDs). The use of VR for AT aims to support levels of interaction and immersion not previously possibly with more traditional AT solutions. Crucial to the success of these technologies is understanding, from the user perspective, the influencing factors that affect the user Quality of Experience (QoE). In addition to the typical QoE metrics, other factors to consider are human behavior like mental and emotional state, posture and gestures. In terms of trying to objectively quantify such factors, there are wide ranges of wearable sensors that are able to monitor physiological signals and provide reliable data. In this demo, we will capture and present the users EEG, heart Rate, EDA and head motion during the use of AT VR application. The prototype is composed of the sensor and presentation systems: for acquisition of biological signals constituted by wearable sensors and the virtual wheelchair simulator that interfaces to a typical LCD display.

A QoE assessment method based on EDA, heart rate and EEG of a virtual reality assistive technology system

Combined speech enhancement and auditory modelling for robust distributed speech recognition

The issue of robustness in the presence of noise is regarded as a significant bottleneck in the commercialisation of speech recognition products, particularly in mobile environments. This paper examines the use of an auditory model combined with a speech enhancement algorithm as a robust front-end for a distributed speech recognition (DSR) system, whereby frontend functionality is implemented on a limited-resource consumer device like a mobile phone, while back-end classifier functionality is carried out by a remote server.

Ronan Flynn

Papers

A QoE evaluation of immersive augmented and virtual reality speech & language assessment applications

A Physiology-Based QoE Comparison of Interactive Augmented Reality, Virtual Reality and Tablet-Based Applications

A QoE assessment method based on EDA, heart rate and EEG of a virtual reality assistive technology system

Combined speech enhancement and auditory modelling for robust distributed speech recognition

Robust distributed speech recognition using speech enhancement