Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology
- URL: http://arxiv.org/abs/2508.18288v1
- Date: Wed, 20 Aug 2025 19:09:15 GMT
- Title: Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology
- Abstract summary: This scoping literature review examines how fairness, bias, and equity are conceptualized and operationalized in Automatic Speech Recognition (ASR)<n>We identify four major areas of inquiry: how researchers understand ASR-related harms.<n>We propose a governance-centered ASR lifecycle as an interdisciplinary framework for responsible ASR development.
- Score: 8.26482039304319
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This scoping literature review examines how fairness, bias, and equity are conceptualized and operationalized in Automatic Speech Recognition (ASR) and adjacent speech and language technologies (SLT) for African American English (AAE) speakers and other linguistically diverse communities. Drawing from 44 peer-reviewed publications across Human-Computer Interaction (HCI), Machine Learning/Natural Language Processing (ML/NLP), and Sociolinguistics, we identify four major areas of inquiry: (1) how researchers understand ASR-related harms; (2) inclusive data practices spanning collection, curation, annotation, and model training; (3) methodological and theoretical approaches to linguistic inclusion; and (4) emerging practices and design recommendations for more equitable systems. While technical fairness interventions are growing, our review highlights a critical gap in governance-centered approaches that foreground community agency, linguistic justice, and participatory accountability. We propose a governance-centered ASR lifecycle as an emergent interdisciplinary framework for responsible ASR development and offer implications for researchers, practitioners, and policymakers seeking to address language marginalization in speech AI systems.
Related papers
- Decolonizing Linguistic Policies in Automated Speech Recognition: A Framework for Cross-Culturally Competent Speech AI [4.893345190925178]
We argue that persistent failures for low-resource, Indigenous, and non-standard language varieties are not only technical errors, but also implicit linguistic policies that reproduce colonial language hierarchies.<n>We introduce the Three Harms (3M) taxonomy and a seven-layer situatedness model for linguistic diversity in ASR and ASR-mediated voice interfaces.
arXiv Detail & Related papers (2026-08-06T15:10:29Z) - Interactive ASR: Towards Human-Like Interaction and Semantic Coherence Evaluation for Agentic Speech Recognition [52.63676763985825]
We propose a semantic-aware evaluation metric to assess recognition quality beyond token-level accuracy.<n>We design an agent framework to simulate human-like multi-turn interaction, enabling iterative refinement of recognition outputs.<n>We will release the code to facilitate future research in interactive and agentic ASR.
arXiv Detail & Related papers (2026-04-10T09:02:42Z) - Large Language Models as a Semantic Interface and Ethical Mediator in Neuro-Digital Ecosystems: Conceptual Foundations and a Regulatory Imperative [83.99688944263843]
This article introduces and substantiates the concept of Neuro-Linguistic Integration (NLI)<n>It is a novel paradigm for human-technology interaction where Large Language Models act as a key semantic interface between raw neural data and their social application.<n>We argue for the development of a second-order neuroethics,' focused not merely on neural data protection but on the ethics of AI-mediated semantic interpretation itself.
arXiv Detail & Related papers (2026-03-18T07:34:17Z) - Applied Sociolinguistic AI for Community Development (ASA-CD): A New Scientific Paradigm for Linguistically-Grounded Social Intervention [0.0]
This paper establishes Applied Sociolinguistic AI for Community Development (ASA-CD) as a novel scientific paradigm for addressing community challenges through linguistically grounded, AI-enabled intervention.<n>ASA-CD introduces three key contributions: (1) linguistic biomarkers as computational indicators of discursive fragmentation; (2) development-aligned natural language processing (NLP), an AI optimisation paradigm prioritising collective outcomes; and (3) a standardised five-phase protocol for discursive intervention.
arXiv Detail & Related papers (2026-01-30T23:12:46Z) - Taxonomy of User Needs and Actions [51.86289485979439]
The growing ubiquity of conversational AI highlights the need for frameworks that capture users' instrumental goals and the situated, adaptive, and social practices through which they achieve them.<n>To address this gap, we introduce the Taxonomy of User Needs and Actions (TUNA), an empirically grounded framework developed through iterative qualitative analysis of 1193 human-AI conversations.<n> TUNA organizes user actions into a three-level hierarchy encompassing behaviors associated with information seeking, synthesis, procedural guidance, content creation, social interaction, and meta-conversation.
arXiv Detail & Related papers (2025-10-07T17:04:42Z) - On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts [69.69818198773244]
We study a range of LMs on both language comprehension and language production.<n>We find that state-of-the-art LMs, but not smaller ones, achieve strong performance on language comprehension.
arXiv Detail & Related papers (2025-09-08T17:59:32Z) - Towards Inclusive Communication: A Unified Framework for Generating Spoken Language from Sign, Lip, and Audio [52.859261069569165]
We propose the first unified framework capable of handling diverse combinations of sign language, lip movements, and audio for spoken-language text generation.<n>We focus on three main objectives: (i) designing a unified, modality-agnostic architecture capable of effectively processing heterogeneous inputs; (ii) exploring the underexamined synergy among modalities, particularly the role of lip movements as non-manual cues in sign language comprehension; and (iii) achieving performance on par with or better than state-of-the-art models specialized for individual tasks.
arXiv Detail & Related papers (2025-08-28T06:51:42Z) - Fairness of Automatic Speech Recognition: Looking Through a Philosophical Lens [0.42970700836450487]
We argue that systematic misrecognition of certain speech varieties constitutes more than a technical limitation.<n>We identify three unique ethical dimensions of speech technologies that differentiate ASR bias from other algorithmic fairness concerns.
arXiv Detail & Related papers (2025-08-10T02:26:47Z) - Recommendations to overcome language barriers in the Vera C. Rubin Observatory Research Ecosystem [0.0]
Report presents recommendations to reduce language barriers within the Vera C. Rubin Observatory Research Ecosystem.<n>English linguistic hegemony in science limits participation and productivity.<n> multilingual presentation formats, academic writing training, a Virtual Writing Center, language support programs, and writing retreats.
arXiv Detail & Related papers (2025-07-24T16:34:26Z) - Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions [4.524096445909663]
Low-resource languages in Africa remain significantly underrepresented in both research and practical applications.<n>This study investigates the major challenges hindering the development of ASR systems for these languages.
arXiv Detail & Related papers (2025-05-16T20:57:39Z) - Igniting Language Intelligence: The Hitchhiker's Guide From
Chain-of-Thought Reasoning to Language Agents [80.5213198675411]
Large language models (LLMs) have dramatically enhanced the field of language intelligence.
LLMs leverage the intriguing chain-of-thought (CoT) reasoning techniques, obliging them to formulate intermediate steps en route to deriving an answer.
Recent research endeavors have extended CoT reasoning methodologies to nurture the development of autonomous language agents.
arXiv Detail & Related papers (2023-11-20T14:30:55Z) - Label Aware Speech Representation Learning For Language Identification [49.197215416945596]
We propose a novel framework of combining self-supervised representation learning with the language label information for the pre-training task.
This framework, termed as Label Aware Speech Representation (LASR) learning, uses a triplet based objective function to incorporate language labels along with the self-supervised loss function.
arXiv Detail & Related papers (2023-06-07T12:14:16Z) - BabySLM: language-acquisition-friendly benchmark of self-supervised
spoken language models [56.93604813379634]
Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels.
We propose a language-acquisition-friendly benchmark to probe spoken language models at the lexical and syntactic levels.
We highlight two exciting challenges that need to be addressed for further progress: bridging the gap between text and speech and between clean speech and in-the-wild speech.
arXiv Detail & Related papers (2023-06-02T12:54:38Z) - Interactive Natural Language Processing [67.87925315773924]
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP.
This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
arXiv Detail & Related papers (2023-05-22T17:18:29Z) - O-Dang! The Ontology of Dangerous Speech Messages [53.15616413153125]
We present O-Dang!: The Ontology of Dangerous Speech Messages, a systematic and interoperable Knowledge Graph (KG)
O-Dang! is designed to gather and organize Italian datasets into a structured KG, according to the principles shared within the Linguistic Linked Open Data community.
It provides a model for encoding both gold standard and single-annotator labels in the KG.
arXiv Detail & Related papers (2022-07-13T11:50:05Z) - Language technology practitioners as language managers: arbitrating data
bias and predictive bias in ASR [0.0]
We use the lens of language policy to analyse how current practices in training and testing ASR systems in industry lead to the data bias giving rise to these systematic error differences.
We propose a re-framing of language resources as (public) infrastructure which should not solely be designed for markets, but for, and with meaningful cooperation of, speech communities.
arXiv Detail & Related papers (2022-02-25T10:37:52Z) - Ethical-Advice Taker: Do Language Models Understand Natural Language
Interventions? [62.74872383104381]
We investigate the effectiveness of natural language interventions for reading-comprehension systems.
We propose a new language understanding task, Linguistic Ethical Interventions (LEI), where the goal is to amend a question-answering (QA) model's unethical behavior.
arXiv Detail & Related papers (2021-06-02T20:57:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.