How do TTS systems handle languages with complex scripts?

Text-to-speech (TTS) systems handle languages with complex scripts through a combination of script-specific preprocessing, linguistic analysis, and synthesis techniques. Complex scripts, such as those in Arabic, Devanagari (used for Hindi), or Mandarin Chinese, often involve features like contextual character shapes, diacritics, or tonal markers that require specialized processing. The first step is normalizing the input text to account for script-specific quirks. For example, Arabic text requires handling right-to-left rendering, resolving optional diacritics (like vowel markers), and managing letterforms that change based on their position in a word. Similarly, Devanagari scripts involve splitting conjunct characters (like “क्” + “ष” = “क्ष”) into individual phonemes, while Mandarin requires converting logographic characters into phonetic representations (like Pinyin) with tone markers.

Linguistic analysis plays a critical role in mapping text to speech sounds. For tonal languages like Mandarin, TTS systems must assign correct pitch contours to syllables based on tone markers (e.g., the high-level tone in “mā” vs. the falling-rising tone in “mǎ”). In Arabic, systems infer missing short vowels (often omitted in written text) using context and grammatical rules. For scripts with complex syllable structures, like Thai or Burmese, TTS engines segment text into syllables using language-specific rules. For example, Thai lacks spaces between words, so the system must identify word boundaries using a dictionary or statistical model. Additionally, languages like Hindi require handling “schwa deletion,” where inherent vowel sounds in consonants are omitted in specific contexts. These steps often rely on rule-based or machine-learning models trained on annotated linguistic data.

Synthesis techniques must adapt to the variability of complex scripts. Neural TTS models (e.g., Tacotron, FastSpeech) use encoder-decoder architectures to map text to spectrograms but require language-specific adaptations. For instance, Mandarin TTS systems might integrate tone embeddings into the model to preserve pitch patterns. For Arabic, models may include separate modules to predict diacritics before synthesis. Low-resource languages pose challenges due to limited training data, but techniques like transfer learning (using a base model trained on a related language) or multilingual training can help. For example, Google’s TTS supports Indic languages by sharing phonetic features across scripts. Finally, rendering engines handle script-specific quirks, such as reordering Arabic glyphs or combining Devanagari characters correctly. Tools like eSpeak-ng use rule-based grapheme-to-phoneme (G2P) conversion for scripts with predictable spelling, while commercial systems like Amazon Polly use hybrid approaches combining rules and deep learning for better accuracy.