Bias and Fairness in Speech Recognition: What Research Says

Speech recognition doesn't work equally well for everyone. If you've ever wondered why your voice assistant struggles with your accent while handling others perfectly, you've encountered speech recognition bias firsthand.

Research consistently shows that commercial speech recognition systems perform worse for certain demographic groups—and understanding these disparities is the first step toward building fairer systems.

What the research shows

Accent and dialect disparities

Multiple studies have found significant accuracy gaps—recent benchmarking of STT services confirms these patterns persist in current systems:

• African American Vernacular English (AAVE) — Error rates up to 2x higher than for Standard American English
• Non-native speakers — Substantially higher error rates, varying by first language
• Regional dialects — Scottish English, Indian English, and other varieties often perform worse than the "standard" accent used in training

We explore why multilingual and accent-aware recognition matters in a separate guide.

Gender differences

Results are mixed but notable:

• Some systems perform better on male voices (reflecting training data composition)
• Others show the reverse, depending on the specific system and test conditions
• Voice characteristics beyond gender (pitch, speaking style) also affect accuracy

Age-related patterns

• Children's voices often have higher error rates
• Elderly speakers may face accuracy issues, especially combined with age-related speech changes

Disability and speech differences

Users with:

• Speech impediments
• Neurological conditions affecting speech
• Communication devices
• Hearing impairments affecting speech production

...often experience dramatically worse accuracy, sometimes rendering systems unusable. See our guide on designing voice features that actually help for accessibility.

Why these biases exist

Speech recognition bias isn't intentional—it emerges from training data and system design:

Training data imbalance

If 80% of training audio comes from speakers with Standard American accents, the system gets very good at that accent and mediocre at everything else. Garbage in, bias out.

Evaluation on narrow benchmarks

Systems are often tuned to perform well on benchmark datasets that don't reflect real-world diversity. A model can score great on LibriSpeech while failing actual users. We cover key STT datasets and their limitations in detail elsewhere.

One-size-fits-all deployment

A single model deployed globally will inevitably work better for some populations than others.

What this means for product teams

If you're building with speech recognition, these disparities affect your users:

Test with diverse speakers

Don't rely solely on internal testing or benchmark scores. Recruit testers who represent your actual user base, including:

• Different accents and dialects
• Various age groups
• Speakers with disabilities or speech differences
• Non-native speakers of your target languages

See our guide on speech robustness research for evaluation methodologies.

Monitor accuracy by segment

Track error rates broken down by user demographics (where you have this data ethically). Word Error Rate (WER) is the standard metric—we explain how WER works and its limitations. If certain groups show consistently worse experiences, that's a product problem to solve.

Offer adaptation and customization

• Voice profiles that learn individual speech patterns
• Accent selection that loads optimized models
• Custom vocabulary for names and terms specific to user contexts

Provide fallbacks

When speech recognition fails, users need alternatives:

• Easy way to type instead
• Human support option
• Clear error messages (not just "didn't catch that")

Be transparent about limitations

If your system works poorly for certain accents or speech patterns, say so. Users deserve to know before they depend on your product.

The path forward

The research community and major providers are working on bias reduction:

• More diverse training data — Intentionally collecting speech from underrepresented groups
• Multi-accent models — Systems designed for variation rather than standardization
• Personalization — Adapting to individual speakers over time
• Fairness metrics — Evaluating accuracy across demographic groups, not just overall

Progress is happening, but slowly. In the meantime, product teams need to account for these limitations in their designs.