Attitudinal Judgments of Dialect Traits and Colorism in African Americans

This study demonstrates how language and complexion influence professional and social perceptions of African Americans. This study contains an online verbal-guise survey where participants either saw a photo of a lighter skin-toned African-American male and female or an electronically darkened version. Audio was attached to each photo, which contains traits of African-American Vernacular English (AAVE) in the case of the male and Standard American English for the female. The results suggest African-American females are more likely to experience colorism in professional traits while African-American males are more likely to experience colorism in social traits. Additionally, the respondent’s race influences perceptions of AAVE.

“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. Specifically, African Americans feel othered when using technology powered by ASR—errors surface thoughts about identity, namely about race and geographic location—leaving them feeling that the technology was not made for them. As a result, African Americans accommodate their speech to have better success with the technology. We incorporate the insights and lessons learned from sociolinguistics in our suggestions for linguistically responsive ways to build more inclusive voice systems that consider African American users’ needs, attitudes, and speech patterns. Our findings suggest that the use of a diary study can enable researchers to best understand the experiences and needs of communities who are often misunderstood by ASR. We argue this methodological framework could enable researchers who are concerned with fairness in AI to better capture the needs of all speakers who are traditionally misheard by voice-activated, artificially intelligent (voice-AI) digital systems.

The role of linguistic structure in the perceptions of vernacular speech

This study explores the role of linguistic structure in speakers’ perceptions of vernacular English, i.e. speech used in informal interactions. In so doing, it tests the assumptions of the Interface Principle (Labov 1993) and its major claim that semantic and discourse-pragmatic features will elicit a greater degree of social awareness than morphosyntactic variants (Levon and Buchstaller 2015). Relying on data obtained from 372 respondents, we explore the social perceptions of two discourse-pragmatic and two morphosyntactic variables. We show that the morphosyntactic features investigated here are generally available to the sociolinguistic monitor of L1 speakers as well as highly advanced learners of English as a Foreign Language. However, these morphosyntactic features are less salient than the semantic/discourse pragmatic variants, and their social indexation is, for this reason, more pliable. We argue for the weaker version of the Interface Principle and propose that the differences in the recognisability of vernacular features is gradient. We additionally propose that juxtaposing different types of speaker data is instrumental in discerning those differences.