Text-to-speech (TTS) systems handle languages with complex scripts through specialized preprocessing, advanced linguistic analysis, and tailored synthesis techniques. Complex scripts—such as those in Arabic, Thai, or Devanagari-based languages—often include features like context-dependent character shapes, non-Latin alphabets, or the absence of explicit word boundaries. To manage these challenges, TTS pipelines incorporate script-specific rules and machine learning models to normalize text, predict pronunciation, and generate natural-sounding speech.
First, TTS systems preprocess text to address script-specific quirks. For example, Arabic requires handling connected letters that change form based on their position in a word, as well as restoring omitted vowels (diacritics) that are critical for accurate pronunciation. Thai lacks spaces between words, so tokenization relies on syllable or word boundary prediction models. Similarly, Japanese mixes kanji (logographic characters), hiragana, and katakana, requiring a combination of dictionary lookups and context analysis to resolve ambiguous readings. These steps ensure the input is standardized before moving to linguistic processing.
Next, linguistic analysis adapts to features like tone, morphology, and prosody. Mandarin Chinese uses tones to distinguish word meanings, so TTS models must embed tone markers into phonetic representations. For agglutinative languages like Turkish or Finnish, systems break down words into morphemes to handle complex suffixes. Prosody modeling—such as stress and intonation—is adjusted for scripts with irregular punctuation or phrasing conventions. For instance, Hindi’s Devanagari script includes inherent vowel sounds that must be explicitly suppressed in certain contexts, requiring precise phonetic mapping to avoid mispronunciations.
Finally, synthesis techniques rely on script-aware datasets and models. Neural TTS systems train on speech data annotated with script-specific phonetic details, like Arabic’s gemination (consonant lengthening) or Thai’s tonal rules. For low-resource languages, transfer learning from related scripts or rule-based grapheme-to-phoneme converters fill gaps in data. Additionally, systems may prioritize certain voice qualities—like clarity in consonant clusters for Georgian—to match linguistic priorities. By combining these strategies, TTS systems balance accuracy and naturalness, even for scripts with intricate writing rules.