View in EDS

Bias in Automatic Speech Recognition: The Case of African American Language

Saved in:
Bibliographic Details
Title: Bias in Automatic Speech Recognition: The Case of African American Language
Language: English
Authors: Martin, Joshua L. (ORCID 0000-0002-8097-3413), Wright, Kelly Elizabeth
Source: Applied Linguistics. Aug 2023 44(4):613-630.
Availability: Oxford University Press. Great Clarendon Street, Oxford, OX2 6DP, UK. Tel: +44-1865-353907; Fax: +44-1865-353485; e-mail: jnls.cust.serv@oxfordjournals.org; Web site: http://applij.oxfordjournals.org/
Peer Reviewed: Y
Page Count: 18
Publication Date: 2023
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Automation, Speech Communication, Black Dialects, Racism, Artificial Intelligence, Racial Discrimination, Linguistics
DOI: 10.1093/applin/amac066
ISSN: 0142-6001
1477-450X
Abstract: Research on bias in artificial intelligence has grown exponentially in recent years, especially around racial bias. Many modern technologies which impact people's lives have been shown to have significant racial biases, including automatic speech recognition (ASR) systems. Emerging studies have found that widely-used ASR systems function much more poorly on the speech of Black people. Yet, this work is limited because it lacks a deeper consideration of the sociolinguistic literature on African American Language (AAL). In this paper, then, we seek to integrate AAL research into these endeavors to analyze ways in which ASRs might be biased against the linguistic features of AAL and how the use of biased ASRs could prove harmful to speakers of AAL. Specifically, we (1) provide an overview of the ways in which AAL has been discriminated against in the workforce and healthcare in the past, and (2) explore how introducing biased ASRs in these areas could perpetuate or even deepen linguistic discrimination. We conclude with a number of questions for reflection and future work, offering this document as a resource for cross-disciplinary collaboration.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1388779
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1388779
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Bias in Automatic Speech Recognition: The Case of African American Language
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Martin%2C+Joshua+L%2E%22">Martin, Joshua L.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8097-3413">0000-0002-8097-3413</externalLink>)<br /><searchLink fieldCode="AR" term="%22Wright%2C+Kelly+Elizabeth%22">Wright, Kelly Elizabeth</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Applied+Linguistics%22"><i>Applied Linguistics</i></searchLink>. Aug 2023 44(4):613-630.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Oxford University Press. Great Clarendon Street, Oxford, OX2 6DP, UK. Tel: +44-1865-353907; Fax: +44-1865-353485; e-mail: jnls.cust.serv@oxfordjournals.org; Web site: http://applij.oxfordjournals.org/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 18
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Evaluative
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+Communication%22">Speech Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Black+Dialects%22">Black Dialects</searchLink><br /><searchLink fieldCode="DE" term="%22Racism%22">Racism</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Racial+Discrimination%22">Racial Discrimination</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistics%22">Linguistics</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1093/applin/amac066
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0142-6001<br />1477-450X
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Research on bias in artificial intelligence has grown exponentially in recent years, especially around racial bias. Many modern technologies which impact people's lives have been shown to have significant racial biases, including automatic speech recognition (ASR) systems. Emerging studies have found that widely-used ASR systems function much more poorly on the speech of Black people. Yet, this work is limited because it lacks a deeper consideration of the sociolinguistic literature on African American Language (AAL). In this paper, then, we seek to integrate AAL research into these endeavors to analyze ways in which ASRs might be biased against the linguistic features of AAL and how the use of biased ASRs could prove harmful to speakers of AAL. Specifically, we (1) provide an overview of the ways in which AAL has been discriminated against in the workforce and healthcare in the past, and (2) explore how introducing biased ASRs in these areas could perpetuate or even deepen linguistic discrimination. We conclude with a number of questions for reflection and future work, offering this document as a resource for cross-disciplinary collaboration.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2023
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1388779
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1388779
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1093/applin/amac066
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 613
    Subjects:
      – SubjectFull: Automation
        Type: general
      – SubjectFull: Speech Communication
        Type: general
      – SubjectFull: Black Dialects
        Type: general
      – SubjectFull: Racism
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Racial Discrimination
        Type: general
      – SubjectFull: Linguistics
        Type: general
    Titles:
      – TitleFull: Bias in Automatic Speech Recognition: The Case of African American Language
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Martin, Joshua L.
      – PersonEntity:
          Name:
            NameFull: Wright, Kelly Elizabeth
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 0142-6001
            – Type: issn-electronic
              Value: 1477-450X
          Numbering:
            – Type: volume
              Value: 44
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Applied Linguistics
              Type: main
ResultId 1