Reviewing Speech Input with Audio

Author:

Hong Jonggi1,Vaing Christine1,Kacorri Hernisa1,Findlater Leah2

Affiliation:

1. University of Maryland, College Park, MD

2. University of Washington, Seattle, WA

Abstract

Speech input is a primary method of interaction for blind mobile device users, yet the process of dictating and reviewing recognized text through audio only (i.e., without access to visual feedback) has received little attention. A recent study found that sighted users could identify only about half of automatic speech recognition (ASR) errors when listening to text-to-speech output of the ASR results. Blind screen reader users, in contrast, may be better able to identify ASR errors through audio due to their greater use of speech interaction and increased ability to comprehend synthesized speech. To compare the experiences of blind and sighted users with speech input and ASR errors, as well as to compare their ability to identify ASR errors through audio-only interaction, we conducted a lab study with 12 blind and 12 sighted participants. The study included a semi-structured interview portion to qualitatively understand experiences with ASR, followed by a controlled speech input task to quantitatively compare participants’ ability to identify ASR errors in their dictated text. Findings revealed differences between blind and sighted participants in terms of how they use speech input and their level of concern for ASR errors (e.g., blind users were more highly concerned). In the speech input task, blind participants were able to identify only 40% of ASR errors, which, counter to our hypothesis, was not significantly different from sighted participants’ performance. In depth analysis of speech input, ASR errors, and strategy of identifying ASR errors scrutinized how participants entered a text with speech input and reviewed it. Our findings indicate the need for future work on how to support blind users in confidently using speech input to generate accurate, error-free text.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,Human-Computer Interaction

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. What I Don’t Like about You?: A Systematic Review of Impeding Aspects for the Usage of Conversational Agents;Interacting with Computers;2024-05-31

2. Uncovering Human Traits in Determining Real and Spoofed Audio: Insights from Blind and Sighted Individuals;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

3. What do Blind and Low-Vision People Really Want from Assistive Smart Devices? Comparison of the Literature with a Focus Study;The 25th International ACM SIGACCESS Conference on Computers and Accessibility;2023-10-22

4. Defining Patterns for a Conversational Web;Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems;2023-04-19

5. MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech;CHI Conference on Human Factors in Computing Systems;2022-04-29

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3