Search arXivSearch

arXiv · 2203.05848

Pardon? An Overview of the Current State and Requirements of Voice User Interfaces for Blind and Visually Impaired Users

Abstract

People with special needs like blind and visually impaired (BVI) people can particularly benefit from using voice assistants providing spoken information input and output in everyday life. However, it is crucial to understand their needs and to include these in the development of accessible and useful assistance systems. By conducting an online survey with 145 BVI people, this paper revealed that common voice assistants like Apple's Siri or Amazon's Alexa are used by a majority of BVI people and are also considered helpful. In particular, features in the context of audio entertainment, internet access and everyday life practical things like weather queries, time-related information (e.g. setting an alarm clock), checking calendar entries and taking notes are particularly often used and appreciated. The participants also indicated that the integration of smart home devices, the optimization of existing functionalities and voice input are important, but also potentially negative aspects such as data privacy and data security are relevant. Therefore, it seems particularly interesting to implement an online data processing as far as possible. Our results contribute to this development by providing an overview of empirically collected requirements for functions and implementation-related aspects.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Christina Oumard, Julian Kreimeier, Timo Götzelmann. 2022-03-11. Pardon? An Overview of the Current State and Requirements of Voice User Interfaces for Blind and Visually Impaired Users. https://arxiv.org/abs/2203.05848

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Are explainable AI (XAI) evaluation strategies aligned? Comparing subjective, objective, and mathematical evaluation measures using saliency maps

The evaluation of explainable AI (XAI) approaches often relies on three families of methods: subjective measures (e.g., questionnaires on trust or satisfaction), objective measures (e.g., task performance metrics), and mathematical metrics (e.g., for faithfulness). Yet, it remains unclear how these families align or diverge in practice. In a{preregistered} between-subjects study (N=166), we use three established saliency map techniques (LIME, Grad-CAM, Guided Backpropagation) as a testbed to examine this issue. We find that each family of methods leads to different conclusions: participants reported no differences in trust or satisfaction, Grad-CAM improved user performance, while mathematical metrics favored Guided Backpropagation. At the same time, mathematical metrics were only partially related to user performance, and these relationships were sometimes counterintuitive. Our findings highlight the methodological importance of comparing subjective, objective, and mathematical approaches when evaluating XAI, illustrating both tensions and aspects that are aligned. We discuss implications for XAI evaluation frameworks.

cs.HC

Mind Your Ps and Qs: Positive Moderation Practice in the Positive Queue

Online communities rely on volunteer moderators to maintain order. Despite their key role, moderators are given a toolbox of punishments and far less support for encouraging contributions they want to see more of. We introduce the Positive Queue as a positive counterpart to Reddit's modqueue: a dedicated space for moderators to discover contributions and behaviors they want to encourage and positively reinforce. With five moderators, four with 6-14 years of experience, we use the Positive Queue to examine how moderators operationalize positive reinforcement. Moderators combined predicted community reception, observed engagement, and their own judgment; used prediction-engagement mismatches to identify overlooked content; and repurposed positive features for punitive and retrospective work. These findings surface tensions around labor, attribution, and community fit. We contribute the Positive Queue as a working system and conceptualize positive moderation as recognition infrastructure that shapes what moderators notice, whose judgment becomes visible, and how recognition reaches contributors.

cs.HC

PILOT: Control Surfaces for Authoring Social Media Feeds

Personalized social media feeds infer preferences from behavior, leaving people little direct control over what they see. Existing controls range from post-level reactions to rules and natural language, but little is known about how people use them together or how added expressiveness changes effort. We built PILOT, a Bluesky feed-authoring system that turns in-feed actions into explicit preferences, deterministic ranking, and inspectable outcomes. A study with seven participants informed an expanded implementation, which we organized into three nested control surfaces that ten participants compared within subjects. Participants assigned controls to distinct jobs: broad controls set direction, in-post actions refined results, and filters removed content. Richer surfaces did not necessarily feel more effortful, and participants' experiences depended more on whether they could verify and repair outcomes. Our findings show that usable feed control requires not simply more controls, but orchestration across mechanisms that support expression, inspection, repair, and episodic use.

cs.HC