A text analysis module in a text-to-speech (TTS) system processes raw input text to prepare it for conversion into speech. It ensures the synthesized speech sounds natural by resolving ambiguities, applying linguistic rules, and generating metadata for pronunciation and prosody. This module acts as the foundation for subsequent steps like phonetic conversion and waveform generation. Below is a breakdown of its core functions:
1. Text Normalization and Preprocessing The module first standardizes the text into a consistent format. This includes expanding abbreviations (e.g., "Dr." to "Doctor"), converting symbols (e.g., "$5" to "five dollars"), and handling numbers, dates, or special characters. For example, "2023-12-25" becomes "December twenty-fifth, twenty twenty-three." It also segments the text into manageable units like sentences or phrases, which helps in applying context-aware rules. Language-specific rules handle edge cases, such as distinguishing "St." as "Street" or "Saint" based on surrounding words.
2. Linguistic Analysis Next, the module performs syntactic and semantic analysis to determine how words function in a sentence. Part-of-speech tagging identifies nouns, verbs, and adjectives, which helps disambiguate homographs (e.g., "read" pronounced as "reed" vs. "red"). Syntactic parsing reveals sentence structure, guiding pauses and intonation. For example, in "I saw the man with the telescope," the placement of pauses clarifies whether the man or the observer has the telescope. Semantic analysis resolves context-dependent terms, like interpreting "cool" as "low temperature" versus "impressive" based on usage.
3. Phonetic Transcription and Prosody Prediction Finally, the module converts normalized text into phonetic representations using pronunciation dictionaries or grapheme-to-phoneme models. For instance, "tomato" might be transcribed as /təˈmeɪtoʊ/ (American) or /təˈmɑːtəʊ/ (British). It also predicts prosodic features like stress, pitch, and rhythm. Machine learning models often analyze sentence structure and context to assign natural-sounding emphasis and pauses. For example, a question ending with "?" might trigger a rising pitch, while a declarative sentence uses a falling pitch.
By addressing these layers, the text analysis module enables the TTS system to produce intelligible and expressive speech. Without it, the output would sound robotic or mispronounce words, undermining usability. Developers often optimize this module using a mix of rule-based systems (for consistency) and statistical models (for handling ambiguity).