Text-to-speech (TTS) providers ensure correct pronunciation of proper nouns through a combination of prebuilt linguistic rules, user customization, and machine learning models. Proper nouns—like names, brands, or locations—often deviate from standard pronunciation rules, so TTS systems use specialized techniques to handle them. For example, a system might default to generic pronunciation rules for common words but apply custom logic for exceptions like “Houston” (pronounced “HYOO-stən” in Texas but “HOW-stən” in New York).
One key method is the use of pronunciation dictionaries or lexicons. These are predefined lists that map words to their phonetic representations using symbols from systems like the International Phonetic Alphabet (IPA). Providers like Amazon Polly or Google Cloud Text-to-Speech maintain extensive lexicons covering common proper nouns. For less common terms, developers can supply custom lexicons via Speech Synthesis Markup Language (SSML). For instance, using SSML’s <phoneme> tag, a developer could specify that “Qt” (a software framework) is pronounced “cute” rather than “cut.” Some providers also allow crowdsourced corrections, where users submit mispronunciations for review and inclusion in updates.
Machine learning plays a role in handling edge cases. Modern TTS systems use neural networks trained on vast audio datasets to predict pronunciations, even for unfamiliar words. These models analyze word structure, context, and language patterns. For example, a model might infer that “Nguyen” (a Vietnamese surname) is pronounced “win” based on training data containing Vietnamese names. However, this approach isn’t foolproof—unusual or newly coined terms (e.g., “X Æ A-12”) may still require manual intervention. To address this, some providers combine automated prediction with fallback rules, such as splitting compound words or checking for known suffixes (e.g., "-ville" in city names).
Finally, TTS systems often let developers override pronunciations programmatically. Services like Microsoft Azure Speech allow custom lexicons to be uploaded via API, ensuring domain-specific terms (like medical jargon or product names) are spoken correctly. For example, a navigation app could ensure “Saint Louis” is pronounced “Saint LOO-iss” instead of “Saint LEW-ee” by linking the term to its phonetic spelling “sənt ˈluːɪs.” These layers of customization, combined with ongoing model training and community feedback, help TTS systems balance accuracy and flexibility for diverse use cases.