Collaborative document editing tools are widely used in professional and academic workplaces. While these tools provide basic accessibility support, it is challenging for blind users to gain collaboration awareness that sighted people can easily obtain using visual cues (e.g., who is editing where and what). Through a series of co-design sessions with a blind coauthor, we identified the current practices and challenges in collaborative editing, and iteratively designed CollabAlly, a system that makes collaboration awareness in document editing accessible to blind users. CollabAlly extracts collaborator, comment, and text-change information and their context from a document and presents them in a dialog box to provide easy access and navigation. CollabAlly uses earcons to communicate background events unobtrusively, voice fonts to differentiate collaborators, and spatial audio to convey the location of document activity. In a study with 11 blind participants, we demonstrate that CollabAlly provides improved access to collaboration awareness by centralizing scattered information, sonifying visual information, and simplifying complex operations.

Collaborative document editing tools are widely used in both professional and academic workplaces. While these tools provide some accessibility features, it is still challenging for blind users to gain collaboration awareness that sighted people can easily obtain using visual cues (e.g., who edited or commented where and what in the document). To address this gap, we present CollabAlly, a browser extension that makes extractable collaborative and contextual information in document editing accessible for blind users. With CollabAlly, blind users can easily access collaborators’ information, track real-time or asynchronous content and comment changes, and navigate through these elements. In order to convey this complex information through audio, CollabAlly uses voice fonts and spatial audio to enhance users’ collaboration awareness in shared documents. Through a series of pilot studies with a coauthor who is blind, CollabAlly’s design was refined to include more information and to be more compatible with existing screen readers.

Supporting voice commands in applications presents significant benefits to users. However, adding such support to existing GUI-based web apps is effort-consuming with a high learning barrier, as shown in our formative study, due to the lack of unified support for creating multi-modal interfaces. We develop Geno---a developer tool for adding the voice input modality to existing web apps without requiring significate NLP expertise. Geno provides a unified workflow for developers to specify functionalities to support by voice (intents), create language models for detecting intents and the relevant information (parameters) from user utterances, and fulfill the intents by either programmatically invoking the corresponding functions or replaying GUI actions on the web app. Geno further supports references to GUI context in voice commands (e.g., "add this to the playlist"). In a study, developers with little NLP expertise were able to add the multi-modal support for two existing web apps using Geno.