Colloquium talk: Dr. Tsung-Lun Alan Wan


Dr. Tsung-Lun Alan Wan is joining us to give a colloquium talk this spring! Please see details of the talk below.

Dr. Tsung-Lun Alan Wan received his PhD from the University of Edinburgh and is a postdoctoral researcher in medical humanities at National Cheng Kung University.  He will be presenting his work on agentive language use among deaf or hard-of-hearing speakers in Taiwan.

Time: April 7, Friday 2023, 8:30-10:30am Eastern Time

Event: Virtual via Zoom


Deaf identity and style-shifting in read speech

Within a medical discourse of disability, deaf ways of speaking spoken languages are approached from pathological perspectives. In this talk, I instead focus on speaker agency among deaf speakers of Taiwan Mandarin in utilizing speech style-shifting to performhearingness/deafness. Looking at the linguistic variable ㄕ sh /ʂ/, in the first part of the talk,I will emphasize the importance of identifying indexical fields of variants from the perspectives of deaf speakers. In the second part of the talk, I will look at topic-based shifting which takes place when deaf speakers read aloud a passage about the oppression upon deaf signers by hearing people. The data show that even if the participants argue deaf speakers should conform to hearing ways of speaking Mandarin, some of them shift to deaf ways of realizing the variable when engaging with the identity politics topic, and the others instead shift to hearing ways of realizing the variable. I argue that this difference in topic effect is mobilized by different stances toward the content of the passage, and the stance-taking is mediated by the presence of a hearing interviewer. 

If you are interested in joining the talk, please email Yongqing ( for the Zoom link.

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Summer Research Opportunities

MI Diaries Research Experience for Undergraduates (REU) 2023

Are you interested in how people tell the stories of their community? 

Or in how the pandemic might have affected the way people speak?

Do you want to gain some research experience?

Apply to join us in summer 2023 at Michigan State Sociolinguistics Lab!

Click here for more information about the MI Diaries Summer 2023 Research Experience for Undergraduates on our project website!

Click here to watch the informal webinar with a presentation by Dr. Betsy Sneller on the details of the MI Diaries Summer 2023 Research Experience for Undergraduates — what it is, how to apply, and Q&A.

NSF Research Experience for Undergraduates (REU)

For students looking for a full-time paid experience, we offer a summer Research Experience for Undergraduates (REU). MI Diaries is a National Science Foundation funded project. We especially encourage students from historically underrepresented groups and/or minority-serving institutions to apply.

  • Location: The Sociolinguistics Lab at Michigan State University‘s East Lansing, MI campus.
  • Eligibility: US citizens registered as undergraduate students in Summer 2023 (depending on the institution, this may include incoming freshmen).
  • Duration: 8 weeks in the summer (June 5 – July 28, 2023).
  • Pay: $600 per week for 30 hours work per week.
  • Background: Students do not need prior linguistics experience to apply!
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Dan Villarreal talk November 3 on auto-coding

Dr. Dan Villarreal (University of Pittsburgh) is visiting the Sociolinguistics Lab in early November. He’ll be giving a talk, open to the public, on Thursday November 3, 2022. Dan’s presentation is of special interest to us because it’s about automating analyses of large-scale datasets. As we build a corpus of Michigan speech in the MI Diaries project, we’ve been using automatic speech recognition (ASR) to speed up our transcription time, and working with MSU’s Institute for Cyber-Enabled Research (ICER) to move some of our data processing to their supercomputer.

Dr. Villarreal is also giving a talk to the SoConDi group at University of Michigan on Nov 4th, 2022, 3-4pm. If you are interested in joining that talk, please contact Yongqing Ye ( or Suzanne Wagner ( for the Zoom link.

Sociolinguistic auto-coding: Applications and pitfalls

Dan Villareal, University of Pittsburgh

Time: Thursday, Nov 3, 4:30-6:15pm

Location: Wells Hall B342 and on Zoom

Zoom link:   Meeting ID: 984 1836 0065 passcode: sociolab.

Researchers in sociophonetics and variationist sociolinguistics have increasingly turned to computational methods to automate time-consuming research tasks such as data extraction (e.g., Fromont & Hay 2012), phonetic alignment (e.g., McAuliffe et al. 2017), and accurate vowel measurement (e.g., Barreda 2021). In this talk, I discuss the advantages and challenges of using sociolinguistic auto-coding (SLAC), a method in which machine learning classifiers assign variants to variable data (Kendall et al. 2021; McLarty, Jones & Hall 2019; Villarreal et al. 2020; Villarreal under review). 

Villarreal et al. (2020) trained random forest classifiers of two sociolinguistic variables of New Zealand English, non-prevocalic /r/ (varying between Present vs. Absent) and intervocalic medial /t/ (Voiced vs. Voiceless), using over 4,000 previously hand-coded tokens (per variable). Cross-validation revealed accuracy rates of 84.5% for /r/ and 91.8% for /t/. In addition to binary predictions, these auto-coders calculate classifier probabilities: the likelihood that a given /r/ token was Present, or a /t/ token was Voiced. In a listening experiment in which 11 phonetically trained listeners coded 60 /r/ tokens, we found a significant positive linear relationship between classifier probability and human judgments; this indicates that classifier probability successfully captures listeners’ perception of phonetically gradient rhoticity. Finally, auto-coders can report which features were most important in classification, helping to shed light on acoustically complex variables like /r/. In short, SLAC can be used for at least three specific functions: binary coding, gradient ‘coding’, and feature selection. 

Like other machine learning (ML) methods, however, there are inherent concerns about SLAC’s fairness—that is, whether it generates equally valid predictions for different speaker groups  (e.g., Koenecke et al. 2020). First, given that there are multiple definitions of ML fairness that are mutually incompatible (Berk et al. 2018; Corbett-Davies et al. 2017; Kleinberg et al. 2017), fairness metrics must be decided upon within individual research domains; I argue for three fairness metrics relevant to the domain of sociolinguistic auto-coding. Second, I re-analyze Villarreal et al.’s (2020) /r/ auto-coder for fairness; I find poor performance on all three fairness metrics, with women’s tokens coded more accurately than men’s (88.8% vs. 81.4%). Third, to remedy these imbalances, I used the same data to test a variety of unfairness-mitigation strategies from the ML fairness literature; I find substantial improvement with respect to fairness, albeit at the expense of predictive performance. 

Given these fairness issues, I reconsider SLAC under Markl’s (2022) premise that some speech and language technologies are too inherently flawed to use. I argue that while SLAC does not fit into this category, its potential users and consumers deserve a “warts and all” awareness of its drawbacks. To that end, I close with concrete recommendations for using SLAC in large-scale research projects. 


Barreda, Santiago. 2021. Fast Track: fast (nearly) automatic formant-tracking using Praat. Linguistics Vanguard 7(1). 

Fromont, Robert & Jennifer Hay. 2012. LaBB-CAT: An annotation store. Proceedings of Australasian Language Technology Association Workshop 113–117. 

Kendall, Tyler, Charlotte Vaughn, Charlie Farrington, Kaylynn Gunter, Jaidan McLean, Chloe Tacata & Shelby Arnson. 2021. Considering performance in the automated and manual coding of sociolinguistic variables: Lessons from variable (ING). Frontiers in Artificial Intelligence 4(43). 

Markl, Nina. 2022. Language variation and algorithmic bias: Understanding algorithmic bias in British English automatic speech recognition. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22), 521–534. New York, NY, USA: Association for Computing Machinery. 

McAuliffe, Michael, Michaela Socolof, Sarah Mihuc, Michael Wagner & Morgan Sonderegger. 2017. Montreal Forced Aligner: Trainable text-speech alignment using Kaldi. In. 

McLarty, Jason, Taylor Jones & Christopher Hall. 2019. Corpus-based sociophonetic approaches to postvocalic r-lessness in African American Language. American Speech 94. 

Villarreal, Dan. under review. Sociolinguistic auto-coding has fairness problems too: Measuring and mitigating bias. Linguistics Vanguard

Villarreal, Dan, Lynn Clark, Jennifer Hay & Kevin Watson. 2020. From categories to gradience: Auto-coding sociophonetic variation with random forests. Laboratory Phonology 11(6). 1–31. 

Continue ReadingDan Villarreal talk November 3 on auto-coding

Colloquium talk: Dr. Annette D’Onofrio

Dr. Annette D’Onofrio is joining us to give a colloquium talk this fall! Please see details of the talk below.

Dr. Annette D’Onofrio is an Assistant Professor in the Linguistics Department at Northwestern University. She will present on her work on Chicagoland project, style, and personae.

Time: Thursday (09/15/2022) 4:30-6:15pm Eastern Time

Event: In-person and Zoom

Talk Abstract

Locating sound change reversal: Racialized and age-based patterns of the Northern Cities Shift in a Chicago community

While dialectological work once indicated that American English regional dialects were becoming increasingly disparate over time (e.g. Labov 2014), recent sociolinguistic studies are revealing the opposite trend in some regions, showing movement away from regionally distinctive language features (e.g. Prichard & Tamminga 2012, Dodsworth & Kohn 2012). Specifically, the Inland North region’s characteristic Northern Cities Vowel Shift (NCS), which had been advancing throughout the 20th century (Labov 2007), has begun to reverse its trajectory in some Inland North locales (Driscoll & Lape 2015; Wagner et al. 2016), including in Chicago (McCarthy 2011, Durian & Cameron 2019). In this talk, I explore the ways in which NCS reversal is socially conditioned in one Chicago neighborhood area. I demonstrate how both broader sociohistorical dynamics of migration and racialization, as well as highly localized oppositions and ideologies, inform patterns of vocalic change in this neighborhood.

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PhD Research Assistantship with MI Diaries

Thinking about PhD studies in language variation and change? 

Want to work on a big linguistic data collection project from your very first semester? 

Interested in five years of funding? 

Apply to Michigan State University’s Linguistics PhD program!  

Come to the Sociolinguistics Lab at Michigan State University! The MSU Linguistics PhD provides a generous 5 years of funding including a stipend, health insurance, and tuition. First year PhD students work part-time as Research Assistants (RAs). The MI Diaries project would love to recruit a strong RA with a research interest in language variation and change to help with our longitudinal study of self-recorded “audio diaries” from hundreds of people across the state. Become involved with everything from project management, community outreach, data analysis, recruitment, mentoring undergraduates and youth interns, to developing best practices for eliciting speech from a broad range of participants. Work closely with our faculty, Prof. Betsy Sneller and Prof. Suzanne Wagner, and with our team of students and other collaborators. Get started on your own related project, so that you’ll have a great foundation for building the research skills you’ll need for your PhD career and beyond. 

Apply here by November 30, 2022 for full consideration for Fall 2023 admission.

Grad student testimonials 

MSU Linguistics graduate students have had great experiences with MI Diaries.

Being involved with the MI Diaries project has enhanced my graduate school experience because it has given me the chance to work on a large-scale collaborative research project. Thanks to this project, I’ve been able to gain knowledge and experiences that can be applied to my own research that I would not have been able to acquire on my own. Working with the MI Diaries has also been incredibly enriching because it has provided me with so many opportunities to deepen my connections with other students and faculty in the department in a professional, but enjoyable setting. It’s also been a great opportunity to mentor undergraduate students and high school students on participating in an academic project and performing linguistic research which has been a personally fulfilling experience.” 

Jack Rechsteiner

“I am able to get hands-on experience of nearly every aspect of a research project — collaboration with faculties and students, mentoring, public outreach, writing, turning research ideas into conference presentations and papers, etc. I am grateful for the professional development opportunities this project offers, as well as all the wonderful personal connections I made working with people in this project. “

Yongqing Ye

“If you are a student interested in sociolinguistics who thrives in a supportive, tight-knit departmental community, continuing your education at MSU is a wonderful choice. In my time here so far, I have not only enjoyed the instruction and guidance of a host of brilliant scholars – including two world-class sociolinguists doing research on the cutting edge – I have also been embedded in one of the most innovative and largest-scale sociolinguistics projects being conducted today. Even after just a year of working in MI Diaries, my knowledge of sociolinguistics, and my ability to both approach research in an ethical, community-conscious manner as well as to operate within a big team of faculty and fellow students, have increased drastically. “

Adam Barnhardt

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