Colin Lea
Apple, Cupertino, California, United States
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 dic- tation, we quantify how dysfluencies impede performance in a consumer-grade speech recognition system. Through three techni- cal 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%.
https://doi.org/10.1145/3544548.3581224
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)