“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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zion Mengesha, Courtney Heldreth, Michal Lahav, Juliana Sublewski, Elyse Tuennerman
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	fair machine learning natural language processing speech to text African American Vernacular English sociolinguistics social psychology Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/608b5ff418ae43a2b343d07afbe00777
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:608b5ff418ae43a2b343d07afbe00777
record_format	dspace
spelling	oai:doaj.org-article:608b5ff418ae43a2b343d07afbe007772021-12-01T09:24:41Z“I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans2624-821210.3389/frai.2021.725911https://doaj.org/article/608b5ff418ae43a2b343d07afbe007772021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/frai.2021.725911/fullhttps://doaj.org/toc/2624-8212Automated 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.Zion MengeshaZion MengeshaCourtney HeldrethMichal LahavJuliana SublewskiElyse TuennermanFrontiers Media S.A.articlefair machine learningnatural language processingspeech to textAfrican American Vernacular Englishsociolinguisticssocial psychologyElectronic computers. Computer scienceQA75.5-76.95ENFrontiers in Artificial Intelligence, Vol 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fair machine learning natural language processing speech to text African American Vernacular English sociolinguistics social psychology Electronic computers. Computer science QA75.5-76.95
spellingShingle	fair machine learning natural language processing speech to text African American Vernacular English sociolinguistics social psychology Electronic computers. Computer science QA75.5-76.95 Zion Mengesha Zion Mengesha Courtney Heldreth Michal Lahav Juliana Sublewski Elyse Tuennerman “I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans
description	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.
format	article
author	Zion Mengesha Zion Mengesha Courtney Heldreth Michal Lahav Juliana Sublewski Elyse Tuennerman
author_facet	Zion Mengesha Zion Mengesha Courtney Heldreth Michal Lahav Juliana Sublewski Elyse Tuennerman
author_sort	Zion Mengesha
title	“I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans
title_short	“I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans
title_full	“I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans
title_fullStr	“I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans
title_full_unstemmed	“I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans
title_sort	“i don’t think these devices are very culturally sensitive.”—impact of automated speech recognition errors on african americans
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/608b5ff418ae43a2b343d07afbe00777
work_keys_str_mv	AT zionmengesha idontthinkthesedevicesareveryculturallysensitiveimpactofautomatedspeechrecognitionerrorsonafricanamericans AT zionmengesha idontthinkthesedevicesareveryculturallysensitiveimpactofautomatedspeechrecognitionerrorsonafricanamericans AT courtneyheldreth idontthinkthesedevicesareveryculturallysensitiveimpactofautomatedspeechrecognitionerrorsonafricanamericans AT michallahav idontthinkthesedevicesareveryculturallysensitiveimpactofautomatedspeechrecognitionerrorsonafricanamericans AT julianasublewski idontthinkthesedevicesareveryculturallysensitiveimpactofautomatedspeechrecognitionerrorsonafricanamericans AT elysetuennerman idontthinkthesedevicesareveryculturallysensitiveimpactofautomatedspeechrecognitionerrorsonafricanamericans
_version_	1718405356291358720

“I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans

Ejemplares similares