> For the complete documentation index, see [llms.txt](https://docs.ixhello.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ixhello.com/ixhc2/emea/emea-deployment.md). # EMEA Deployment ## LLM Vendor Differences: US vs EMEA This article outlines the supported Large Language Model (LLM), Speech-to-Text (STT), and Text-to-Speech (TTS) vendors for EMEA deployments, and highlights how they differ from US deployments. EMEA deployments are restricted to a region-specific subset of vendors and models to ensure compliance with data residency, regulatory, and infrastructure requirements. URL : *** ### 1. LLM (Large Language Models) * Supports low-latency, streaming-based conversational use cases. Available Models: * GPT Realtime (gpt-realtime) *** ### 2. OpenAI Non-Realtime (openai, text-in/text-out) * Supports standard request-response based interactions. Available Models: * GPT 4o Mini (gpt-4o-mini) * GPT 5 (gpt-5) * GPT 5.1 (gpt-5.1) * GPT 5.2 (gpt-5.2) * GPT-5 Mini (gpt-5-mini) *** ### 3. STT (Speech-to-Text) Supported Vendors * Azure OpenAI Speech-to-Text (azure-openai-stt) * Used for transcription of audio input into text. Available Models: * GPT-4o Transcribe (gpt-4o-transcribe) * GPT-4o Mini Transcribe (gpt-4o-mini-transcribe) *** ### 4. TTS (Text-to-Speech) Supported Vendors * Azure OpenAI TTS (azure-openai-tts) * Provides neural voice synthesis for real-time and batch TTS use cases. Available Models: * TTS 1 (tts-1) *** ### 5. Amazon Polly (amazon-polly) Supports a wide range of voices and synthesis technologies across multiple languages. Supported Model Types * **Generative (G):** Best for the most human-like, conversational, emotionally expressive speech—ideal for assistants, marketing, and customer-facing experiences.\ \&#xNAN;*Language support:* Available in **single-language** and **multi-language** voices, enabling seamless code-switching and broader audience reach. * **Neural (N):** Best for high-quality, natural-sounding speech in general-purpose applications such as narrations, virtual assistants, and product interfaces.\ \&#xNAN;*Language support:* Primarily **single-language** voices, with select options supporting **multiple languages or accents**. * **Long-form (L):** Optimized for extended content like audiobooks, training materials, and videos, ensuring consistent pacing, tone, and clarity over long durations.\ \&#xNAN;*Language support:* Typically, **single-language** voices to maintain consistency, with limited **multi-language** support depending on the model. * **Standard (S):** Designed for cost-effective, large-scale text-to-speech scenarios where basic intelligibility is sufficient and naturalness is less critical.\ \&#xNAN;*Language support:* Mostly **single-language** voices with broad language coverage, but without cross-language switching capabilities. **Available Voices:**


Voice	Gender	Voice Categories	Type	Language(s)
Aditi	Female	Standard	Multi-language	Hindi, Indian English
Adriano	Male	Neural	Single-language	Italian
Alba	Female	Long-form	Single-language	Spanish
Amy	Female	Generative / Neural / Standard	Multi-language	German, US English, British English, Spanish
Andrés	Male	Generative / Neural	Multi-language	Spanish, US English, Portuguese
Aria	Female	Neural	Single-language	US English
Arlet	Female	Neural	Single-language	Spanish
Arthur	Male	Neural	Single-language	British English
Astrid	Female	Standard	Single-language	Swedish
Ayanda	Female	Generative / Neural	Multi-language	German, Nigerian English
Bianca	Female	Generative / Neural / Standard	Multi-language	Brazilian Portuguese, Portuguese, US English, Spanish
Brian	Male	Neural / Standard	Multi-language	US English, Spanish
Burcu	Female	Neural	Single-language	Turkish
Camila	Female	Generative / Neural / Standard	Multi-language	Brazilian Portuguese, US English, Spanish
Carla	Female	Standard	Single-language	Spanish
Carmen	Female	Standard	Single-language	Spanish
Céline	Female	Generative / Neural	Multi-language	French, Swiss French
Chantal	Female	Standard	Single-language	Canadian French
Conchita	Female	Standard	Single-language	Spanish
Cristiano	Male	Standard	Single-language	Portuguese
Daniel	Male	Generative / Neural	Multi-language	German, US English, Norwegian
Danielle	Female	Generative / Neural / Long-form	Multi-language	German, US English, Dutch
Dóra	Female	Standard	Single-language	Hungarian
Elin	Female	Neural	Single-language	Swedish
Emma	Female	Neural / Standard	Multi-language	US English, British English
Enrique	Male	Standard	Single-language	Spanish
Ewa	Female	Generative / Standard	Multi-language	German, Polish
Filiz	Female	Standard	Single-language	Turkish
Gabrielle	Female	Generative / Neural	Multi-language	German, US English, French
Geraint	Male	Standard	Single-language	British English
Giorgio	Male	Standard	Single-language	Italian
Gregory	Male	Neural / Long-form	Multi-language	Norwegian, Dutch
Gwyneth	Female	Standard	Single-language	Welsh
Hala	Female	Neural	Single-language	Arabic
Hannah	Female	Generative / Neural	Multi-language	German, US English
Hans	Male	Standard	Single-language	German
Hiujin	Female	Neural	Single-language	Korean
Ida	Female	Neural	Single-language	Norwegian Bokmål
Inês	Female	Neural / Standard	Multi-language	Portuguese, Spanish
Isabelle	Female	Generative / Neural	Multi-language	German, US English, French
Ivy	Female	Neural / Standard	Multi-language	US English, British English
Jacek	Male	Standard	Single-language	Polish
Jan	Male	Standard	Single-language	Swedish
Jasmine	Female	Neural	Single-language	US English
Jihye	Female	Neural	Single-language	Korean
Jitka	Female	Neural	Single-language	Czech
Joanna	Female	Generative / Neural / Standard	Multi-language	German, US English, British English
Joey	Male	Neural / Standard	Multi-language	Norwegian, British English
Justin	Male	Neural / Standard	Multi-language	Norwegian, British English
Kajal	Female	Generative / Neural	Multi-language	German, US English, Hindi
Karl	Male	Standard	Single-language	Swedish
Kazuha	Female	Neural	Single-language	Japanese
Kendra	Female	Neural / Standard	Multi-language	US English, British English
Kevin	Male	Neural	Single-language	US English
Kimberly	Female	Neural / Standard	Multi-language	US English, British English
Laura	Female	Generative / Neural	Multi-language	German, Norwegian, Dutch
Léa	Female	Generative / Neural / Standard	Multi-language	German, US English, French, Swiss French
Liam	Male	Generative / Neural	Multi-language	German, US English
Lisa	Female	Generative / Neural	Multi-language	German, Norwegian
Liv	Female	Standard	Single-language	Swedish
Lotte	Female	Standard	Single-language	Danish
Lucia	Female	Generative / Neural / Standard	Multi-language	German, US English, Spanish, European Spanish
Lupe	Female	Generative / Neural / Standard	Multi-language	German, US English, Spanish, European Spanish
Mads	Male	Standard	Single-language	Danish
Maja	Female	Standard	Single-language	Polish
Marlene	Female	Standard	Single-language	German
Mathieu	Male	Standard	Single-language	French
Matthew	Male	Generative / Neural / Standard	Multi-language	German, US English, British English, Australian English
Maxim	Male	Standard	Single-language	Russian
Mia	Female	Generative / Neural / Standard	Multi-language	German, US English, Swedish, Norwegian
Miguel	Male	Standard	Single-language	Spanish
Mizuki	Female	Standard	Single-language	Japanese
Naja	Female	Standard	Single-language	Danish
Niamh	Female	Generative / Neural	Multi-language	German, US English, Irish English
Nicole	Female	Standard	Single-language	Australian English
Ola	Female	Generative / Neural	Multi-language	German, US English, Norwegian
Olivia	Female	Generative / Neural	Multi-language	German, US English, British English
Patrick	Male	Long-form	Single-language	Canadian French
Pedro	Male	Generative / Neural	Multi-language	German, US English, Spanish, US Spanish
Penélope	Female	Standard	Single-language	Spanish
Raul	Male	Long-form	Single-language	Brazilian Portuguese
Raveena	Female	Standard	Single-language	Indian English
Rémi	Male	Generative / Neural	Multi-language	German, US English, French
Ricardo	Male	Standard	Single-language	Brazilian Portuguese
Ruben	Male	Standard	Single-language	Dutch
Russell	Male	Standard	Single-language	Australian English
Ruth	Female	Generative / Neural / Long-form	Multi-language	German, US English, British English, Australian English
Sabrina	Female	Neural	Single-language	Swiss German
Salli	Female	Generative / Neural / Standard	Multi-language	German, US English, British English
Seoyeon	Female	Generative / Neural / Standard	Multi-language	German, US English, Korean
Sergio	Male	Generative / Neural	Multi-language	German, US English, Spanish
Sofie	Female	Neural	Single-language	Belgian Dutch
Stephen	Male	Generative / Neural	Multi-language	German, US English
Suvi	Female	Neural	Single-language	Finnish
Takumi	Male	Neural / Standard	Multi-language	Japanese, US English
Tatyana	Female	Standard	Single-language	Russian
Thiago	Male	Neural	Single-language	Brazilian Portuguese
Tomoko	Female	Neural	Single-language	Japanese
Vicki	Female	Generative / Neural / Standard	Multi-language	German, US English, British English, Australian English
Vitória	Female	Neural / Standard	Multi-language	Portuguese, Brazilian Portuguese
Zayd	Male	Neural	Single-language	Arabic
Zeina	Female	Standard	Single-language	Arabic
Zhiyu	Female	Neural / Standard	Multi-language	Mandarin Chinese, Cantonese Chinese

--- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.ixhello.com/ixhc2/emea/emea-deployment.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.