Language coverage

This page is the canonical reference for which languages PolyAI supports and which models cover them across LLM, speech recognition (ASR), and text-to-speech (TTS). For setup and runtime behavior, see Multi-language. For model selection, see Model and Raven.

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At a glance

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LLM language coverage

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Raven (recommended)

Raven is purpose-built for customer service across voice and chat. Raven 3.5 supports the following 24 languages: Arabic, Bulgarian, Cantonese, Croatian, Czech, Dutch, English, French, German, Greek, Hindi, Hindi (Romanized/Hinglish), Italian, Japanese, Korean, Mandarin (PRC), Mandarin (Taiwan), Polish, Portuguese (Brazil), Portuguese (Portugal), Serbian, Spanish (US), Swedish, Turkish. Strongest performance relative to general-purpose models: Cantonese, Italian, Korean, Mandarin (China), Mandarin (Taiwan), Spanish (US). You can keep prompts and knowledge in English and set the response language to your target language – Raven responds consistently in the target language. Quality improves further if you translate prompts and add examples in the target language.

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Third-party LLMs

When you select an OpenAI or Amazon Bedrock model on the Model page, language coverage matches that provider’s official support. Use a third-party LLM when:

• Your target language is not on the Raven list above, or
• You need a capability only the third-party model provides.

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Full list of supported response languages

PolyAI accepts the BCP 47 codes below as response languages. Pass the code in the UI or via conv.set_language().

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Models

For full model descriptions and selection guidance, see Model.

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LLM models

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ASR providers

ASR providers wired into the platform today:

• Deepgram
• Google Cloud Speech-to-Text (v1 and v2)
• NVIDIA Riva
• Amazon Transcribe
• OpenAI (Whisper)
• Fano
• NVIDIA NeMo

The platform routes requests to the best-fit provider per language and use case, with automatic fallback. See Keyphrases.

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TTS providers

TTS voice availability varies by provider and language. Browse what’s available per language in the Voice library.

• ElevenLabs
• Amazon Polly
• Azure Speech
• Cartesia
• Google Cloud Text-to-Speech
• Hume
• MiniMax
• Neuphonic
• OpenAI
• PlayHT
• Rime
• Custom TTS integrations

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Choosing a language and model

1. Pick the response language from the table above using its BCP 47 code.
2. Check Raven coverage – if your language is on the 24-language Raven list, Raven 3.5 is the recommended LLM.
3. If Raven does not cover it (for example Danish), select a third-party LLM in Voice configuration or Chat configuration. Verify the chosen model officially supports the language.
4. Confirm regional availability for Bedrock models if you are deploying outside US-1.
5. Confirm a voice exists for the language in the Voice library. Prefer native voices over multilingual fallbacks.
6. Configure ASR – defaults usually work; for domain terms see Keyphrases and ASR biasing.

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Related pages

• Multi-language – set up, test, and route multilingual agents.
• Model – compare LLM options and configure per channel.
• Raven – PolyAI’s proprietary LLM family.
• Bring your own model – connect a custom LLM endpoint.
• Keyphrases – ASR settings and biasing.
• Voice library – browse voices per language and provider.