Linguistic preprocessing is a foundational step in text-to-speech (TTS) systems that converts raw input text into structured data suitable for generating natural-sounding speech. It ensures the TTS engine accurately interprets the text’s meaning, pronunciation, and context before synthesizing audio. Without this step, the system might mispronounce words, mishandle abbreviations, or fail to convey intended emphasis, leading to unnatural or unintelligible output. Preprocessing bridges the gap between written language and spoken speech by analyzing and transforming text into linguistic features like phonemes, stress patterns, and sentence structure.
A key task in linguistic preprocessing is text normalization, which standardizes inconsistencies in written text. For example, numbers, symbols, and abbreviations must be converted into their spoken equivalents. The text “I bought 3 items for $20” becomes “I bought three items for twenty dollars.” Similarly, “Dr. Smith lives on Maple St.” might expand to “Doctor Smith lives on Maple Street.” Homographs—words spelled the same but pronounced differently—also require context-aware resolution. The word “read” in “I will read the book” versus “I read the book yesterday” needs different phonetic representations. Part-of-speech tagging and syntactic analysis help disambiguate these cases by examining surrounding words.
Another critical function is prosody modeling, where the system assigns rhythm, stress, and intonation to the speech. Punctuation marks like commas or question marks influence pauses and pitch changes. For instance, “Let’s eat, Grandma!” versus “Let’s eat Grandma!” requires distinct phrasing to avoid ambiguity. Additionally, preprocessing identifies emphasis markers (e.g., capitalization or italics) or syntactic boundaries to guide the TTS engine’s intonation. For languages with complex morphology, like German compound nouns or Mandarin tone sandhi, preprocessing ensures correct pronunciation rules are applied. Errors at this stage—such as misinterpreting “St.” as “Saint” instead of "Street"—can drastically alter meaning. By structuring text into linguistically meaningful units, preprocessing enables TTS systems to produce coherent, context-aware speech output.