![]() ![]() We report on the baseline performance of the endpointer and ASR models for dysfluent speech, and investigate three interventions to better support PWS at different points in the workflow: tuning the endpointer model that predicts when the user is done speaking, tuning the decoder component of the ASR model, and refining dysfluencies in the transcribed ASR output. Propose and show the impact of multiple technical improvements to support PWS in using speech technologyįigure 1: Simplified workflow and components of a voice assistant, which can be accessed through a smart speaker or other speech-enabled devices.Connect those experiences to performance across a range of stuttering severities.Our work to improve speech recognition for PWS is the first to: The three interventions are shown in Figure 1 within a typical VA pipeline. Then, we describe and evaluate three technical solutions that apply production-oriented improvements to a consumer-grade automated speech recognition (ASR) system to better support PWS. We report on user experience and system performance with speech technology for PWS, including a survey on the use of speech technology and an investigation of voice assistant (VA) and dictation system performance. This article focuses on one subset of this population: people who stutter (PWS). Yet speech presents barriers for many people with communication disabilities such as stuttering, dysarthria, or aphasia. Speech interaction is especially important for devices with smaller or no screens, such as smart speakers and smart headphones, that support speech interaction. 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 recognition systems have improved substantially in recent years, leading to widespread adoption across computing platforms.
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