Niteshift

Mon, Nov 3, 2025 22:00 PM

Info

Hosts: Mike Sargent, Co-Hosts:, Leighann Lord (comedian) , Dr. Kelly Elizabeth Wright (linguist)

Topics:

Discrimination by Ear/The Word of the Year -
Everyday Examples -
Linguistic Racism/ Linguistic Profiling -
“Discrimination by ear.” -
Weaponized Cognitive Dissonance -
Accent bias in hiring and housing calls (Dr. John Baugh's & Dr. Kelly’s experiments). -
changing tone for different crowds (reading the room) -
Accent, Race & Regional Bias -
Spanish - accent profiling in the U.S.
• Citizens detained for “sounding foreign.” -
• Voice recognition algorithms and ICE auditory bias. -
Gender Bias -
Policing Speech -
Reclaiming Language & Cultural Ownership -

Synopsis:

THEME:

Discrimination by Ear: How You Sound = Who You Are
Weaponized Dissonance and the Sound of Otherness

Guests:

Playlist:

Info / Links:

LEIGHANN: https://www.veryfunnylady.com/
KELLY: https://lsa.umich.edu/linguistics/news-events/all-news/search-news/phd-alumna-kelly-e--wright-publishes-paper-on-bias-in-automatic-.html
Research
Stanford University / Dr. John Baugh (2003–present):
Building on his original linguistic profiling work, Baugh and others have shown that accent alone affects perceived credibility and employability.
Washington Post (April 6, 2023):
Reported that speech-recognition AI and automated hiring software often misclassify or downgrade non–Standard American English accents, especially Spanish-accented English, African American English, and Southern dialects.
Source: https://www.washingtonpost.com/technology/2023/04/06/ai-accent-bias/
MIT Technology Review (Nov 2021): “The Algorithms That Rank Your Voice.”
Found that call-center analytics platforms evaluate “clarity” and “trust score” partly based on accent, penalizing non-native English speakers.
Source: https://www.technologyreview.com/2021/11/02/voice-analysis-bias/
Stanford Computational Linguistics Group (2022):
Noted that automated “accent-neutralization” tools in hiring platforms reproduce existing racial hierarchies under the guise of efficiency.
Source: https://nlp.stanford.edu

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