View publication

Dysfluencies and variations in speech pronunciation can severely degrade speech recognition performance, and for many individuals with moderate-to-severe speech disorders, voice operated systems do not work. Current speech recognition systems are trained primarily with data from fluent speakers and as a consequence do not generalize well to speech with dysfluencies such as sound or word repetitions, sound prolongations, or audible blocks. The focus of this work is on quantitative analysis of a consumer speech recognition system on individuals who stutter and production-oriented approaches for improving performance for common voice assistant tasks (i.e., "what is the weather?"). At baseline, this system introduces a significant number of insertion and substitution errors resulting in intended speech Word Error Rates (isWER) that are 13.64% worse (absolute) for individuals with fluency disorders. We show that by simply tuning the decoding parameters in an existing hybrid speech recognition system one can improve isWER by 24% (relative) for individuals with fluency disorders. Tuning these parameters translates to 3.6% better domain recognition and 1.7% better intent recognition relative to the default setup for the 18 study participants across all stuttering severities.

Related readings and updates.

Improved Speech Recognition for People Who Stutter

Speech recognition systems have improved substantially in recent years, leading to widespread adoption across computing platforms. Two common forms of speech interaction are voice assistants (VAs) that listen for spoken commands and respond accordingly, and dictation systems, which act as an alternative to a keyboard by converting the user's open-ended speech to written text for messages, emails, and so on. Speech interaction is especially important for devices with smaller or no screens, such as smart speakers and smart headphones, that support speech interaction. Yet speech presents barriers for many people with communication disabilities such as stuttering, dysarthria, or aphasia.

See article details

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

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…
See paper details