English

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

Human-Computer Interaction 2023-02-28 v3

Abstract

Consumer speech recognition systems do not work as well for many people with speech diferences, 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 frst address these questions using results from a 61-person survey from people who stutter and fnd participants want to use speech recognition but are frequently cut of, 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 dysfuencies 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 of 79.1% less often and improves word error rate from 25.4% to 9.9%.

Keywords

Cite

@article{arxiv.2302.09044,
  title  = {From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition},
  author = {Colin Lea and Zifang Huang and Lauren Tooley and Jaya Narain and Dianna Yee and Panayiotis Georgiou and Tien Dung Tran and Jeffrey P. Bigham and Leah Findlater},
  journal= {arXiv preprint arXiv:2302.09044},
  year   = {2023}
}

Comments

CHI 2023

R2 v1 2026-06-28T08:43:00.168Z