Linguistic preprocessing in text-to-speech (TTS) systems converts raw text into a structured format that the synthesis engine can use to generate natural-sounding speech. It ensures the system correctly interprets abbreviations, numbers, symbols, and context-dependent words before converting them to audio. Without this step, the TTS might mispronounce terms, produce unnatural pauses, or fail to convey intended meaning, leading to robotic or confusing output.
The process begins with text normalization, where raw text is standardized. This includes expanding abbreviations (e.g., "Dr." to "Doctor"), converting numbers to words (e.g., "2023" to "twenty twenty-three"), and handling symbols (e.g., "$100" to "one hundred dollars"). Next, homograph disambiguation resolves words with multiple pronunciations based on context. For example, determining whether "read" should rhyme with "bed" (past tense) or "bead" (present tense) requires analyzing surrounding words or grammatical structure. Phonetic transcription then maps normalized text to phonemes (language-specific sound units), using pronunciation dictionaries or rules. For instance, "cat" becomes /k/ /æ/ /t/, while exceptions like "through" (vs. "tough") rely on language-specific models. Finally, prosody modeling predicts rhythm, stress, and intonation by analyzing punctuation, sentence structure, and semantic emphasis to make speech sound natural.
For example, the sentence "He sold the item for $100 in 2023" undergoes normalization to "He sold the item for one hundred dollars in twenty twenty-three." The system then determines the correct pronunciation of "sold" as /soʊld/ and applies prosody to emphasize "sold" and "one hundred dollars." Challenges include resolving ambiguous cases (e.g., "St." as "Saint" or "Street") and adapting rules across languages. Linguistic preprocessing is foundational to TTS quality, ensuring the synthesized speech is accurate, context-aware, and human-like.