About

Alayo Tripp, Ph.D., is a faculty member in the Department of Linguistics at the University of Florida. They use the pronouns they/them and received their Ph.D. from the University of Maryland, College Park. Their research focuses on social cognition, phonology, sociophonetics, and developmental psycholinguistics.

Research topics

• Sociology
• Psychology
• Computer Science
• Artificial Intelligence
• Cognitive psychology
• Social Science
• Social psychology
• Epistemology
• Developmental psychology
• Gender studies
• Linguistics

Selected publications

• The impact of social bias on adults' learning of the pronunciation of a new language - Native speakerhood, Naming, Teacher/Student (Whose Input experiment 5b)

  Open Science Framework · 2026-01-01

  otherOpen accessSenior author

  This study investigates the effect of listeners’ perceptions of accents and how those perceptions impact language acquisition. Specifically, we are interested in how a pervasive racialized standard of a “native speaker” can impede language learning because of social bias. We will conduct a naming experiment, studying implicit bias. We hypothesize that participants’ preference for the speech of a teacher will be modulated by beliefs about their "native speakerhood". Our objective is to replicate our recent finding that participants’ degree of preference for a speaker's pronunciation over that of another speaker is reduced when they are told that the speaker is a “nonnative” as opposed to a “native” speaker of the language.

  PublisherDOI

• The impact of social bias on adults' learning of the pronunciation of a new language - Teacher/Student, Teacher's RacializedAppearance, 2AFC, Teacher "native speaker" status (Whose Input experiment 6)

  Open MIND · 2026-01-01

  otherOpen accessSenior author

  This study investigates the effect of listeners’ perceptions of accents and how those perceptions impact language acquisition. Specifically, we are interested in how a pervasive racialized standard of a “native speaker” can impede language learning because of social bias. We will conduct a two-alternative forced-choice accent preference experiment, studying this bias. We hypothesize that participants will show a preference for the test talker’s productions that are more similar to the teacher’s accent, with Black depictions evoking a smaller effect than White depictions. We expect participants will vary in their beliefs about “Western European languages”, accents and the goals of language learning, and these beliefs will influence their accent preferences. Our objective is to extend our recent finding that participants’ degree of preference for a speaker's pronunciation over that of another speaker is reduced when they are told that the speaker is a “nonnative” as opposed to a “native” speaker of the language.

  PublisherDOI

• Finding our ROLE: How and why to reframe essentialist approaches to language

  2026-01-17

  articleOpen access

  Essentialist categorizations of language users, such as NATIVE SPEAKER, are widely used but lack empirical validity and reinforce social inequities. This article focuses on the NATIVENESS construct, critically examining how its centrality in social-scientific research distorts scholarly inquiry, introduces bias in educational and clinical assessments, and perpetuates exclusion in academia. We argue that such labels impose artificial homogeneity, devalue linguistic diversity, and contribute to systemic biases in society. By reifying social divisions, essentialist categorizations can exclude marginalized groups, perpetuate linguistic discrimination, and hinder scientific progress. We advocate for a shift away from essentialist proxies and toward more contextually grounded and empirically driven characterizations of language use. A reflexive and interdisciplinary approach is necessary to dismantle these harmful frameworks and promote more accurate, inclusive, and equitable research. Our argument is relevant not just to the cognitive sciences, but to any scholarship which involves describing or understanding language. Ultimately, rejecting essentialist assumptions will lead to more nuanced understandings of language, identity, and social belonging, fostering both scientific and societal transformation by promoting justice and accuracy across social-scientific disciplines.

  PublisherDOI

• Finding our ROLE: How and why to reframe essentialist approaches to language

  PsyArXiv (OSF Preprints) · 2026-01-15

  preprintOpen access

  Essentialist categorizations of language users, such as NATIVE SPEAKER, are widely used but lack empirical validity and reinforce social inequities. This article focuses on the NATIVENESS construct, critically examining how its centrality in social-scientific research distorts scholarly inquiry, introduces bias in educational and clinical assessments, and perpetuates exclusion in academia. We argue that such labels impose artificial homogeneity, devalue linguistic diversity, and contribute to systemic biases in society. By reifying social divisions, essentialist categorizations can exclude marginalized groups, perpetuate linguistic discrimination, and hinder scientific progress. We advocate for a shift away from essentialist proxies and toward more contextually grounded and empirically driven characterizations of language use. A reflexive and interdisciplinary approach is necessary to dismantle these harmful frameworks and promote more accurate, inclusive, and equitable research. Our argument is relevant not just to the cognitive sciences, but to any scholarship which involves describing or understanding language. Ultimately, rejecting essentialist assumptions will lead to more nuanced understandings of language, identity, and social belonging, fostering both scientific and societal transformation by promoting justice and accuracy across social-scientific disciplines.

  Publisher

• Finding our ROLE: How and why to reframe essentialist approaches to language

  Cognition · 2026-01-14

  article
  PublisherDOI

• The impact of social bias on adults' learning of the pronunciation of a new language

  Open MIND · 2025-01-01

  otherOpen access

  This repository contains study materials, experiment files, data and analysis code for a series of ongoing studies investigating the impact of social bias on adults’ learning of the pronunciation of a new language.

  Publisher

• CODEOFCONDUCT at Multilingual Counterspeech Generation: A Context-Aware Model for Robust Counterspeech Generation in Low-Resource Languages

  arXiv (Cornell University) · 2025-01-01

  preprintOpen accessSenior author

  This paper introduces a context-aware model for robust counterspeech generation, which achieved significant success in the MCG-COLING-2025 shared task. Our approach particularly excelled in low-resource language settings. By leveraging a simulated annealing algorithm fine-tuned on multilingual datasets, the model generates factually accurate responses to hate speech. We demonstrate state-of-the-art performance across four languages (Basque, English, Italian, and Spanish), with our system ranking first for Basque, second for Italian, and third for both English and Spanish. Notably, our model swept all three top positions for Basque, highlighting its effectiveness in low-resource scenarios. Evaluation of the shared task employs both traditional metrics (BLEU, ROUGE, BERTScore, Novelty) and JudgeLM based on LLM. We present a detailed analysis of our results, including an empirical evaluation of the model performance and comprehensive score distributions across evaluation metrics. This work contributes to the growing body of research on multilingual counterspeech generation, offering insights into developing robust models that can adapt to diverse linguistic and cultural contexts in the fight against online hate speech.

  PublisherOA PDFDOI

• Probabilistic linear search model for lexical selection response times: Assessing phonemic structure through processing asymmetries across languages

  The Journal of the Acoustical Society of America · 2025-10-01

  articleSenior author

  This study introduces a statistical model formalizing the relationship between lexical selection response times (RT) and phonemic structure. Word reconstruction task research has explored potentially universal lexical access processes, where listeners convert nonwords into real words by altering a single phoneme (vowel, consonant, or tone). Previous studies focus on univariate analyses of behavior, such as effects of functional load, phoneme inventory size, or phonemic frequency, presenting hypotheses that do not jointly predict empirical data (i.e., response times) across languages. The present model describes a probabilistic linear search of a language's phonemic inventory, where segment selection is guided by estimated phoneme frequencies (formalized with Plackett–Luce weights). Applying the model to Mandarin, English, and other languages shows that differences in RT between categories may be best explained primarily by contrasts in inventory size. This model is also able to predict lower RT in the free-choice condition as a consequence of higher neighborhood density compared to conditions demanding changes to tones, consonants, or vowels alone. Our results suggest that processing asymmetries (i.e., vowel mutability) are not intrinsic to phonemic structure but emerge from stimulus design. The generalizability of this model surpasses many popular theories and enhances our understanding of lexical selection.

  PublisherDOI

• PANDA -- Paired Anti-hate Narratives Dataset from Asia: Using an LLM-as-a-Judge to Create the First Chinese Counterspeech Dataset

  arXiv (Cornell University) · 2025-01-01 · 1 citations

  preprintOpen accessSenior author

  Despite the global prevalence of Modern Standard Chinese language, counterspeech (CS) resources for Chinese remain virtually nonexistent. To address this gap in East Asian counterspeech research we introduce the a corpus of Modern Standard Mandarin counterspeech that focuses on combating hate speech in Mainland China. This paper proposes a novel approach of generating CS by using an LLM-as-a-Judge, simulated annealing, LLMs zero-shot CN generation and a round-robin algorithm. This is followed by manual verification for quality and contextual relevance. This paper details the methodology for creating effective counterspeech in Chinese and other non-Eurocentric languages, including unique cultural patterns of which groups are maligned and linguistic patterns in what kinds of discourse markers are programmatically marked as hate speech (HS). Analysis of the generated corpora, we provide strong evidence for the lack of open-source, properly labeled Chinese hate speech data and the limitations of using an LLM-as-Judge to score possible answers in Chinese. Moreover, the present corpus serves as the first East Asian language based CS corpus and provides an essential resource for future research on counterspeech generation and evaluation.

  PublisherOA PDFDOI

• PANDA – Paired Anti-hate Narratives Dataset from Asia: Using an LLM-as-a-Judge to Create the First Chinese Counterspeech Dataset

  Qeios · 2025-02-19 · 1 citations

  preprintOpen accessSenior author

  Despite the global prevalence of Modern Standard Chinese language, counterspeech (CS) resources for Chinese remain virtually nonexistent. To address this gap in East Asian counterspeech research we introduce a corpus of Modern Standard Mandarin counterspeech that focuses on combating hate speech in Mainland China. This paper proposes a novel approach of generating CS by using an LLM-as-a-Judge, simulated annealing, LLMs zero-shot CN generation and a round-robin algorithm. This is followed by manual verification for quality and contextual relevance. This paper details the methodology for creating effective counterspeech in Chinese and other non-Eurocentric languages, including unique cultural patterns of which groups are maligned and linguistic patterns in what kinds of discourse markers are programmatically marked as hate speech (HS). In our analysis of the generated corpora, we provide strong evidence for the lack of open-source, properly labeled Chinese hate speech data and the limitations of using an LLM-as-Judge to score possible answers in Chinese. Moreover, the present corpus serves as the first East Asian language based CS corpus and provides an essential resource for future research on counterspeech generation and evaluation.1

  PublisherOA PDFDOI

Frequent coauthors

• Benjamin Munson

  University of Minnesota

  18 shared

• Rachel Hayes‐Harb

  University of Utah

  6 shared

• Shannon Barrios

  University of Utah

  4 shared

• Enengy Schutt

  2 shared

• Matthew B. Winn

  University of Minnesota System

  2 shared

• Eleanor Nickel

  University of Minnesota

  2 shared

• Tatiana Lyons

  2 shared

• Paras Bhagwat Bassuk

  1 shared

Education

• Ph.D., Linguistics

  University of Maryland, College Park

  2019