TTS providers ensure correct pronunciation of proper nouns through a combination of predefined linguistic rules, custom pronunciation dictionaries, and user-controlled overrides. Proper nouns, such as names, brands, or locations, often defy standard pronunciation rules, so TTS systems use specialized techniques to handle them accurately.
First, many TTS systems include built-in phonetic dictionaries that map words to their phonetic representations. For common proper nouns (e.g., "Paris" or "Google"), these dictionaries provide predefined pronunciations based on language-specific rules or widely accepted usage. For less common terms, providers may use algorithms to predict pronunciation using grapheme-to-phoneme (G2P) models, which analyze letter patterns. However, these predictions can fail for unconventional names (e.g., "X Æ A-12"), so providers often allow developers to define custom pronunciations using markup languages like SSML (Speech Synthesis Markup Language). For example, Amazon Polly lets users specify phonemes directly in SSML to override default pronunciations.
Second, some TTS platforms offer user-editable pronunciation dictionaries. Developers or end-users can upload lists of proper nouns with their correct phonetic spellings, which the TTS system prioritizes during synthesis. This is critical for applications like navigation systems, where street names or cities must be pronounced accurately. For instance, a voice assistant in a car might use a custom dictionary to ensure "Houston Street" in New York is pronounced "HOW-stən" instead of the city’s "HYOO-stən." Enterprise solutions often include tools for bulk uploading and managing these custom entries.
Finally, context and language settings play a role. TTS systems detect the text’s language or regional dialect to apply appropriate pronunciation rules. For multilingual content, providers may use language identification algorithms or rely on explicit metadata (e.g., setting lang="fr" in SSML for French). Additionally, some providers crowdsource corrections or use machine learning to refine pronunciations over time. For example, if users frequently correct "Nike" from "nyke" to "ny-kee," the system might update its model. However, this requires balancing user input with validation to avoid errors. Overall, the goal is to combine automation with flexibility, allowing developers to ensure accuracy where it matters most.