StammerApp

McNaney, Róisín; Bull, Christopher; Mackie, Lynne; Dahman, Floriane; Stringer, Helen; Richardson, Dan; Welsh, Daniel

doi:10.1145/3173574.3173841

Cited by 9 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The tool uses active learning to identify words that the user is likely to encounter issues with, and suggests alternative words that may be easier to speak; no user study has been reported. Finally, McNaney et al [49] employed a multi-stakeholder co-design process to design StammerApp, a mobile app that allows users to set goals, practice, and reflect on their experiences in especially challenging situations. While the project did not focus on speech input, it provides a qualitative understanding of a total of 39 participants' experiences with stammering, including specific challenges (e.g., talking on the phone), and a general desire to be able to "stammer openly and feel more confident and relaxed with their own voice. "…”

Section: Accessibility Of Speech Recognition Systemsmentioning

confidence: 99%

From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition

Lea¹,

Huang²,

Tooley³

et al. 2023

Preprint

View full text Add to dashboard Cite

Consumer speech recognition systems do not work as well for many people with speech differences, such as stuttering, relative to the rest of the general population. However, what is not clear is the degree to which these systems do not work, how they can be improved, or how much people want to use them. In this paper, we first address these questions using results from a 61-person survey from people who stutter and find participants want to use speech recognition but are frequently cut off, misunderstood, or speech predictions do not represent intent. In a second study, where 91 people who stutter recorded voice assistant commands and dictation, we quantify how dysfluencies impede performance in a consumer-grade speech recognition system. Through three technical investigations, we demonstrate how many common errors can be prevented, resulting in a system that cuts utterances off 79.1% less often and improves word error rate from 25.4% to 9.9%. CCS CONCEPTS• Human-centered computing → Empirical studies in accessibility.

show abstract

Section: Accessibility Of Speech Recognition Systemsmentioning

confidence: 99%

From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition

Lea¹,

Huang²,

Tooley³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Respiratory Self-Care

Tendedez

Ferrario

McNaney

et al. 2019

Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare

Self Cite

View full text Add to dashboard Cite

Chronic respiratory conditions (CRCs) are life-long diseases affecting millions of people worldwide. They have a huge impact on individuals' everyday lives, resulting in a number of physical and emotional challenges. Self-management interventions for CRCs are thought to provide empowerment and improve quality of life. However, despite the number of people living with CRCs, most self-management tools in previous HCI work have been designed without the insight of those affected by the conditions. In this paper, we contribute to the literature by investigating the experiences and everyday challenges faced by those with CRCs, through the involvement of 156 participants via interviews and an anonymous survey. Our findings reveal the self-care challenges of CRCs and the reactive management approaches taken by participants. We conclude by providing a set of design implications that support the design of future self-management tools for CRCs. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Interaction design process and methods; User centered design

show abstract

Speech diversity and speech interfaces

Clark

Cowan

Roper

et al. 2020

Proceedings of the 2nd Conference on Conversational User Interfaces

View full text Add to dashboard Cite

The number of speech interfaces and services made available through them continue to grow. This has opened up interactions to people who rely on speech as a critical modality for interacting with systems. However, people with diverse speech patterns such as those who stammer are at risk of being negatively affected or excluded from speech interface interaction. In this paper, we consider what an inclusive speech interface future may look like for people who stammer. In doing so, we identify three key challenges: (1) developing effective speech recognition, (2) understanding the user experiences of people who stammer and (3) supporting speech interfaces designers through appropriate heuristics. We believe the interdisciplinary and cross-community strengths of venues like CUI are well positioned to address these challenges going forward. CCS CONCEPTS • Human-centered computing → Accessibility technologies; Natural language interfaces.

show abstract

StammerApp

Cited by 9 publications

References 33 publications

From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition

From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition

Respiratory Self-Care

Speech diversity and speech interfaces

Contact Info

Product

Resources

About