The Complete Guide to Multilingual AI Dialogue

The single biggest lever is picking an engine actually trained natively on the target language, rather than a broad multilingual generalist with a language flag bolted on. A voice engine trained specifically on a language family will consistently beat a 30-language generalist on prosody and on the specific consonants and vowel sounds that language actually contends with — a generalist with the wrong underlying accent reading the right words is instantly recognizable as dubbing, even when every word is technically correct.

Decide the language and the engine before worrying about cost — cost differences between engines are real, but choosing the wrong engine to save a fraction of a cent per line is the single most common way non-English dialogue ends up needing a dozen retries. Same discipline as hard rule 5 in the the 14 hard rules: decide the audio route before you generate.

The nukta problem

Some words in a Hindi TTS engine can come back mispronounced even when everything else — the language setting, the script, the surrounding sentence — is completely correct. A real, confirmed example: the word मज़ा (with a nukta diacritic, the dot under ज that shifts its sound) was consistently mispronounced by one Indic-native engine. Two fixes both worked: respelling it without the nukta as मजा, and embedding it inside a fuller phrase (अरे, बहुत मजा आया) rather than sending it alone. Neither is a language-setting problem — it's a narrower, word-level gap in the engine's pronunciation model, and it generalizes: if a specific word keeps coming back wrong regardless of correct language and script, try a phonetically simpler respelling before assuming the whole line needs a different approach.

The script-and-performance-language interaction on video models

Separately from pronunciation, a quoted Hindi line written in Devanagari script, combined with heavy performance-direction language in the same prompt, has tripped at least one major video model's content-safety filter outright — not a pronunciation issue, a filter block. Romanizing the line (writing it in Latin transliteration) and softening the performance-direction language cleared it on the retry, with no other change. See language limitations for the full writeup.

Native video-model voices reading Hindi

Third-party testing of one popular video model's native voice pipeline reports Hindi coming back with a noticeably more robotic read and weaker lip-sync accuracy than its core-tier languages. This is a real, reported pattern — worth knowing about before you trust a video model's own voice for a Hindi line where the read has to be convincing. See video models compared for what to use instead.

Modern Standard Arabic is normally written without full vowel marking — the short-vowel diacritics (tashkeel: fatha, kasra, damma, sukun, shadda) are routinely omitted in everyday written text, the same way an English reader doesn't need every vowel spelled phonetically to read a word correctly. A text-to-speech engine doesn't have that context the way a fluent reader does, and has to guess the missing vowels — which tends to fail exactly on names, technical terms, and anything outside common vocabulary.

The likely fix, by analogy with the Hindi nukta case: if an Arabic line comes back mispronounced and a simpler respelling doesn't fix it, try writing the line with the tashkeel diacritics included rather than assuming the word itself is the problem. This hasn't been tested against a real generation yet — it's the logical next thing to try, grounded in how Arabic TTS systems generally handle ambiguity, not a confirmed Ekly finding.

This is the same family of fix as the Hindi nukta case and German's compound-word problem (below): when a specific word is the failure, respelling it to something the engine already pronounces correctly beats fighting the model on the original spelling.

Tone accuracy is a separate, open question. Academic research on Mandarin text-to-speech is clear that tone modeling matters a great deal for intelligibility, and some dedicated Chinese TTS systems accept pinyin with tone marks as a correction input. Whether a given engine's plain text field honors an inline pinyin hint the same way, or only reads the characters directly, is untested — if you hit a tone-related mispronunciation, try the homophone-substitution fix above first, and treat an inline pinyin hint as worth experimentally trying, not something to rely on.

The reassuring case: modern multilingual voice engines handle German's umlauts (ä, ö, ü) and the sharp-s (ß) correctly as a matter of course, and most compound words resolve fine too. The one place it still breaks down is genuinely long, rare technical compounds — Bundesausbildungsförderungsgesetz-length words — which occasionally trip even good engines.

The fix is the same principle as everywhere else on this page: hyphenate or split the compound into its component words rather than sending the fused giant word and hoping. This applies to any language not covered on its own subpage here — French, Spanish, and most widely-spoken languages are well-served by broad multilingual engines by default; the respelling/splitting principle is the first thing to reach for if a specific word still comes back wrong.

This is the page every other language subpage points to for "so what do I actually do."

When a word absolutely has to be said a specific way — a brand name, a proper noun with no more "obvious" spelling — here's the hierarchy of techniques, roughly in order of how much lift each one takes:

For the ElevenLabs-specific version of phoneme-tag support (which models, which alphabets), see delivery.