Decolonizing Linguistic Policies in Automated Speech Recognition: A Framework for Cross-Culturally Competent Speech AI

• URL: http://arxiv.org/abs/2608.06141v1
• Date: Thu, 06 Aug 2026 15:10:29 GMT
• Title: Decolonizing Linguistic Policies in Automated Speech Recognition: A Framework for Cross-Culturally Competent Speech AI
• Abstract summary: 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.
• Score: 4.893345190925178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper focuses on automatic speech recognition (ASR) and ASR-mediated voice interfaces that shape access to public services, healthcare, and education. 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. Drawing on linguistic capital, raciolinguistic ideology, language policy research, and decolonial computing, we show how data, metrics, and model priors determine whose voices become machine-legible. We introduce the Three Harms (3M) taxonomy---Misrecognition, Misalignment, and Mistrust---and a seven-layer situatedness model for linguistic diversity in ASR and ASR-mediated voice interfaces. We then propose a participatory framework and minimum audit protocol for culturally competent ASR, positioning affected communities as co-designers, evaluators, and governance partners.

Related papers

• MUSCAT: MUltilingual, SCientific ConversATion Benchmark [60.87925076316812]
  The goal of multilingual speech technology is to facilitate seamless communication between individuals speaking different languages.<n>To create this experience, speech technology needs to address several challenges: Handling mixed multilingual input, specific vocabulary, and code-switching.<n>We propose a new benchmark to evaluate current Automatic Speech Recognition (ASR) systems, whether they are able to handle these challenges.
  arXiv  Detail & Related papers  (2026-04-17T10:39:01Z)
• Speak in Context: Multilingual ASR with Speech Context Alignment via Contrastive Learning [5.770962296305264]
  We introduce a context-aware multilingual ASR framework that supports diverse languages and accents.<n>Our approach combines a frozen speech encoder and a decoder-only language model via a lightweight projection module.<n>We show that contextual input consistently improves recognition quality.
  arXiv  Detail & Related papers  (2026-03-06T17:37:06Z)
• Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity [0.0]
  Speech remains one of the most visible yet overlooked vectors of inclusion and exclusion in contemporary society.<n>This article focuses on the structural biases that shape perceptions of atypical speech and are now being encoded into artificial intelligence.
  arXiv  Detail & Related papers  (2026-01-26T16:12:25Z)
• WESR: Scaling and Evaluating Word-level Event-Speech Recognition [59.21814194620928]
  Speech conveys not only linguistic information but also rich non-verbal vocal events such as laughing and crying.<n>We develop a refined taxonomy of 21 vocal events, with a new categorization into discrete (standalone) versus continuous (mixed with speech) types.<n>Based on the refined taxonomy, we introduce WESR-Bench, an expert-annotated evaluation set (900+ utterances) with a novel position-aware protocol.
  arXiv  Detail & Related papers  (2026-01-08T02:23:21Z)
• 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)
• Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology [8.26482039304319]
  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.
  arXiv  Detail & Related papers  (2025-08-20T19:09:15Z)
• 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)
• SpeechIQ: Speech Intelligence Quotient Across Cognitive Levels in Voice Understanding Large Language Models [76.07833875692722]
  Speech-based Intelligence Quotient (SIQ) is a new form of human cognition-inspired evaluation pipeline for voice understanding large language models.<n>Our framework represents a first-of-its-kind intelligence examination that bridges cognitive principles with voice-oriented benchmarks.
  arXiv  Detail & Related papers  (2025-07-25T15:12:06Z)
• Towards Unsupervised Speech Recognition Without Pronunciation Models [57.222729245842054]
  In this article, we tackle the challenge of developing ASR systems without paired speech and text corpora.<n>We experimentally demonstrate that an unsupervised speech recognizer can emerge from joint speech-to-speech and text-to-text masked token-infilling.<n>This innovative model surpasses the performance of previous unsupervised ASR models under the lexicon-free setting.
  arXiv  Detail & Related papers  (2024-06-12T16:30:58Z)
• 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)

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.