Why Speech-to-Text Gets It Wrong, and How to Fix It (2026)

Common Speech-to-Text Failures and Solutions

Speech-to-text technology frequently fails in six specific areas. Understanding these failure points allows developers to implement targeted fixes.

• Names and Rare Words: Speech-to-text models prioritize common words they have heard most frequently. When encountering rare words or brand names, they often substitute similar-sounding common words. The Fix: Provide a short, relevant list of expected words—such as names, products, or jargon—using custom vocabulary, keyword boosting, or key term prompting.
• Numbers: Similar-sounding numbers (like "fifteen" and "fifty") are easily confused, and a single incorrect digit can invalidate phone or card numbers. Additionally, models must determine how to format spoken numbers (inverse text normalization). The Fixes: Read numbers back to the caller for confirmation, validate the expected length or format of the number, or allow users to input digits via their keypad.
• Noise: Background noise, multiple speakers, or the system's own voice reflecting back into the microphone can corrupt transcriptions. The Fixes: Implement echo cancellation and noise removal. Because heavy filtering can degrade speech quality, test recognition accuracy with noise removal both enabled and disabled.
• Phone Audio: Standard phone calls restrict audio frequencies to under 3,400 Hz, cutting off the higher frequencies needed to distinguish sounds like "s" and "f". The Fixes: Utilize models or settings optimized specifically for phone audio, and conduct testing using actual phone calls rather than clean recordings.
• Accents and Languages: Models perform best on the demographics they were trained on most. Significant accuracy gaps exist for speakers with strong accents, those using less common languages, or individuals mixing multiple languages. The Fixes: Test systems using voices that represent actual callers, and track accuracy metrics per demographic group rather than relying on a single overall average.
• Silence and Hallucinations: Certain models, particularly those using attention decoders, may transcribe invented phrases during periods of silence or background noise. The Fixes: Use a voice activity detector to filter out silence so only actual speech is processed, and require confirmation before acting on critical information.

Measuring System Performance

Standard Word Error Rate (WER) metrics treat all errors equally, meaning a minor error (like transcribing "the" incorrectly) carries the same weight as a critical error (like an incorrect digit in a phone number). To properly evaluate a system, developers should measure entity accuracy—specifically tracking whether names, numbers, and dates are correct—and test using real customer calls rather than standard benchmarks.