A Developer's Guide to Fixing Common TTS Pronunciation Errors

Production text-to-speech pronunciation errors commonly stem from context-dependent heteronyms, unfamiliar domain or brand terminology, ambiguous alphanumeric strings, and failures to normalize dates, currencies, abbreviations, and other structured text. Targeted SSML phoneme tags can override individual pronunciations when supported, while centralized PLS lexicons are better suited to frequently used domain vocabularies, although provider support and activation methods differ. Text preprocessing offers the most portable solution across TTS platforms by converting difficult inputs into explicit spoken forms, such as spelling out IDs, expanding currency values, and applying locale-specific date formats; it is also the primary control method for systems such as Deepgram Aura-2 that rely on input formatting rather than SSML. Effective implementations select methods according to error frequency, latency, maintenance capacity, and platform features, then validate changes through regression testing, Word Error Rate targets below 5%, subjective quality scoring, and production A/B tests.

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