Beyond Acoustics: Isolating Dialectal and Sociolinguistic Bias in Spanish ASR

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Natural Language Processing · Depth: Expert, quick

Summary

An evaluation of the Whisper large-v3 Automatic Speech Recognition (ASR) system on 276 recordings from the Corpus Oral y Sonoro del Español Rural (COSER) reveals significant dialectal and sociolinguistic biases. While aggregate Word Error Rate (WER) appears competitive, disaggregating WER by speaker role (Informants vs. Interviewers) uncovers systematic disparities. The study found that mixed-role evaluation underestimates Informant WER, particularly in southern Spanish provinces. Negative Binomial regression showed Informants from Andalusia generated 1.20 times more errors (p < 0.001) and Extremadura 1.24 times more errors (p = 0.020) compared to the Castilian heartland. Furthermore, male Informants produced 12.5% more errors than females (p < 0.001) after geographic adjustment. These findings underscore that aggregate benchmarks suppress disparities disproportionately affecting underrepresented speaker populations, necessitating role-disaggregated evaluation for fair ASR system audits.

Key takeaway

For NLP Engineers or AI Ethicists deploying ASR systems in sociolinguistically diverse populations, your current aggregate Word Error Rate (WER) metrics are likely insufficient. You should implement role-disaggregated evaluation, separating performance for distinct speaker groups like "Informants" and "Interviewers," to accurately identify and address biases. This approach will prevent systematic underestimation of errors for underrepresented populations, ensuring fairer and more robust ASR deployments.

Key insights

Aggregate ASR benchmarks systematically suppress sociolinguistic disparities, requiring disaggregated evaluation for fairness.

Principles

• Role-disaggregated evaluation is a prerequisite for ASR fairness audits.
• Aggregate ASR metrics can conceal allocative harm and measurement failure.
• Dialectal and sociolinguistic factors significantly impact ASR performance.

Method

Compute ASR Word Error Rate (WER) separately for Informants and Interviewers within recordings to reveal hidden biases.

In practice

• Apply role-disaggregated WER analysis to ASR systems.
• Focus fairness audits on specific speaker demographics.

Topics

• ASR Bias
• Whisper large-v3
• Sociolinguistics
• Dialectal Variation
• Fairness Audits
• Word Error Rate

Best for: Research Scientist, AI Scientist, NLP Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.