African American English speakers' pitch variation and rate adjustments for imagined technological and human addressees.

This paper examines the adaptations African American English speakers make when imagining talking to a voice assistant, compared to a close friend/family member and to a stranger. Results show that speakers slowed their rate and produced less pitch variation in voice-assistant-"directed speech" (DS), relative to human-DS. These adjustments were not mediated by how often participants reported experiencing errors with automatic speech recognition.

"I don't Think These Devices are Very Culturally Sensitive."-Impact of Automated Speech Recognition Errors on African Americans.

Automated speech recognition (ASR) converts language into text and is used across a variety of applications to assist us in everyday life, from powering virtual assistants, natural language conversations, to enabling dictation services. While recent work suggests that there are racial disparities in the performance of ASR systems for speakers of African American Vernacular English, little is known about the psychological and experiential effects of these failures paper provides a detailed examination of the behavioral and psychological consequences of ASR voice errors and the difficulty African American users have with getting their intents recognized. The results demonstrate that ASR failures have a negative, detrimental impact on African American users.

Racial disparities in automated speech recognition.

Automated speech recognition (ASR) systems, which use sophisticated machine-learning algorithms to convert spoken language to text, have become increasingly widespread, powering popular virtual assistants, facilitating automated closed captioning, and enabling digital dictation platforms for health care. Over the last several years, the quality of these systems has dramatically improved, due both to advances in deep learning and to the collection of large-scale datasets used to train the systems. There is concern, however, that these tools do not work equally well for all subgroups of the population.