BEGIN:VCALENDAR VERSION:2.0 PRODID:-//events.la.psu.edu//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:STANDARD DTSTART:20201101T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD BEGIN:DAYLIGHT DTSTART:20200308T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:15442-90e70fd7b0694587cffaf7595b5fd87d@events.la.psu.edu DTSTAMP:20261015T080235Z DTSTART;TZID=America/New_York:20250613T090000 DTEND;TZID=America/New_York:20250613T110000 SUMMARY:African Studies Global Virtual Forum: Decoloniality and Southern Epistem ologies—Joshua L. Martin and Kelly Elizabeth Wright DESCRIPTION:\nTitle: “Linguistic Bias in Automatic Speech Recognition: T he Case of African American Language ”\n\nAbstract:\n\nResearch on bias in artificial intelligence has grown exponentially in recent years\, esp ecially around racial bias. Many modern technologies that impact people’ s lives have been shown to have significant racial biases\, including au tomatic speech recognition (ASR) systems. Emerging studies have found th at widely used ASR systems function much more poorly on the speech of Bl ack people. Yet\, this work is limited because it lacks a deeper conside ration of the sociolinguistic literature on African American Language (A AL). In this talk\, then\, we seek to integrate AAL research into these endeavors to analyze ways in which ASRs might be biased against the ling uistic features of AAL and how the use of biased ASRs could prove harmfu l to speakers of AAL. Specifically\, we (1) will provide an overview of the ways in which AAL has been discriminated against in the workforce an d healthcare in the past\, and (2) explore how introducing biased ASRs i n these areas could perpetuate or even deepen linguistic discrimination. We will conclude with a number of questions for reflection and future w ork.\n\nBio:\n\nJoshua L. Martin (University of Florida) is a linguist w ho has researched the intersection of language\, race\, and technology\, particularly linguistic bias against African American Language in autom atic speech recognition systems. His recent work includes “Bias in autom atic speech recognition: The case of African American Language” (with Ke lly E. Wright)\, “Understanding Racial Disparities in Automatic Speech R ecognition: The Case of Habitual ‘Be’” and “Spoken Corpora Data\, Automa tic Speech Recognition\, and Bias Against African American Language: The case of Habitual 'Be'”. Martin can be reached at joshua.martin@ufl.edu. \n\nKelly Elizabeth Wright (University of Wisconsin-Madison) is a Black Biracial cisgender woman from Appalachia. She is an experimental socioli nguist researching linguistic racism across institutions through mixed m ethodologies including perceptual experimentation\, machine learning\, a nd ethnographic fieldwork. Her recent work includes “Language and Discri mination: Generating Meaning\, Perceiving Identities\, and Discriminatin g Outcomes”. She is also a contributing editor to American Speech's Amon g the New Words column. Wright can be reached at kewright4@wisc.edu.\n\n For more details: https://events.la.psu.edu/event/afr-global-virtual-for um-joshua-l-martin-kelly-wright/ X-ALT-DESC;FMTTYPE=text/html:

Title: “Linguis tic Bias in Automatic Speech Recognition: The Case of African American L anguage ”

Abstract:

Research on bias in artificial intellige nce has grown exponentially in recent years, especially around racial bi as. Many modern technologies that impact people’s lives have been shown to have significant racial biases, including automatic speech recognitio n (ASR) systems. Emerging studies have found that widely used ASR system s function much more poorly on the speech of Black people. Yet, this wor k is limited because it lacks a deeper consideration of the sociolinguis tic literature on African American Language (AAL). In this talk, then, w e seek to integrate AAL research into these endeavors to analyze ways in which ASRs might be biased against the linguistic features of AAL and h ow the use of biased ASRs could prove harmful to speakers of AAL. Specif ically, we (1) will provide an overview of the ways in which AAL has bee n 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 will conclude with a numbe r of questions for reflection and future work.

Bio:

Joshua L . Martin (University of Florida) is a linguist who has researched the in tersection of language, race, and technology, particularly linguistic bi as against African American Language in automatic speech recognition sys tems. His recent work includes “Bias in automatic speech recognition: Th e case of African American Language” (with Kelly E. Wright), “Understand ing Racial Disparities in Automatic Speech Recognition: The Case of Habi tual ‘Be’” and “Spoken Corpora Data, Automatic Speech Recognition, and B ias Against African American Language: The case of Habitual 'Be'”. Marti n can be reached at joshua.martin@ufl.edu.

Kelly Elizabeth Wright (University of Wisconsin-Madison) is a Black Biracial cisgender woman fr om Appalachia. She is an experimental sociolinguist researching linguist ic racism across institutions through mixed methodologies including perc eptual experimentation, machine learning, and ethnographic fieldwork. He r recent work includes “Language and Discrimination: Generating Meaning, Perceiving Identities, and Discriminating Outcomes”. She is also a cont ributing editor to American Speech's Among the New Words column. Wright can be reached at kewright4@wisc.edu.

For more details: https://events.la.psu.edu/event/afr-global-virtual-forum- joshua-l-martin-kelly-wright/

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