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

arXiv:2608.06141 · cs.CL, cs.CY, cs.HC · Submitted 2026-08-06 · Read on arXiv

Jay L. Cunningham, Mark Atta Mensah, Richard Martinez, Joao Vieira da Silva Neto, Efi Dawodu

cs.CL, cs.CY, cs.HC

Submitted: 2026-08-06

Comments: 10 Pages, 2 Figures, 2 Tables, Interspeech 2026 - Sydney, Australia

License: http://creativecommons.org/licenses/by/4.0/

The gist: This paper focuses on automatic speech recognition (ASR) and ASR-mediated voice interfaces that shape access to public services, healthcare, and education.

Terminology

Benchmark — 14× in this paper · explained in 2 episodes
Accuracy — 8× in this paper · explained in 1 episode
Accountability — 4× in this paper · explained in 2 episodes
Benchmarking — 3× in this paper · explained in 4 episodes
Generative AI — 3× in this paper · explained in 2 episodes
Obfuscation — 3× in this paper · explained in 2 episodes
Automatic Speech Recognition (ASR) — 2× in this paper · explained in 2 episodes
Composition — 2× in this paper · explained in 2 episodes
Failure Modes — 2× in this paper · explained in 3 episodes
Meeting of the Association for Computational Linguistics — 2× in this paper
Word Error Rate — 2× in this paper
Computer-Supported Cooperative Work and Social Computing — 1× in this paper
Conference on Human Factors in Computing Systems — 1× in this paper
Conformity — 1× in this paper · explained in 2 episodes
Core — 1× in this paper · explained in 2 episodes
Error Taxonomy — 1× in this paper · explained in 3 episodes
Evaluation Framework — 1× in this paper · explained in 3 episodes
Feedback Loop — 1× in this paper · explained in 2 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Grounding — 1× in this paper · explained in 3 episodes
Low-Resource Languages — 1× in this paper · explained in 4 episodes
Mozilla Data Collective — 1× in this paper
Performance Gap — 1× in this paper · explained in 2 episodes
Representation Learning — 1× in this paper · explained in 3 episodes
Tone Error Rate — 1× in this paper
Transformer — 1× in this paper · explained in 2 episodes
Verification — 1× in this paper · explained in 2 episodes

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.

Terminology

Abstract

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