Georgian TTS
Turn reviewed Georgian text into spoken content and voice-assistant replies.
Ertaoza · Our models
Our own Georgian AI models: TTS, STT and LLM
Ertaoza has its own TTS, STT and LLM models. Together, they support the speech and language work behind a Georgian conversation. This overview explains what each model does so you can start with the right component rather than treating every AI task as the same problem.
Three components of a spoken interaction
STT
Speech → text
LLM
Text and context → response
TTS
Response text → speech
Which model does your task need?
Use TTS when the input is written text and the desired output is speech. Use STT when the input is a recording or spoken request and you need text. Use an LLM to process language and context. A conversational assistant can combine all three; a text-to-audio task may only need TTS.
Compare the three model roles
Georgian STT
Speech recognition for Georgian transcripts and the input to a spoken conversation.
Georgian LLM
Language processing between a person’s request, approved context and a useful response.
TTS: from Georgian text to speech
Text-to-speech turns written content into a spoken output. It belongs in voice replies, audio versions of articles and spoken instructions. In a project review, listen for the pronunciation of names and numbers, phrasing and whether the reading is easy to follow.
Georgian TTS — text to speechSTT: from Georgian speech to text
Speech-to-text, also called automatic speech recognition or ASR, produces text from speech. It can serve transcription or provide the input to a voice assistant. Evaluation should include the actual channel, background noise and terminology the system will encounter.
Georgian STT — speech recognitionLLM: language and context
A large language model processes text and helps generate a context-aware response. An LLM can work with the output of STT and prepare a response for TTS. It is not automatically a source of verified business facts and does not by itself authorize changes in external systems.
Georgian LLM — language modelChoose a model by the input and the result
For an audio version of an existing article, begin with a reviewed text and TTS. For a transcript of a conversation, begin with STT and a correction workflow. For questions about approved business information, define the knowledge source and the LLM’s answer boundaries.
For a full voice agent, also define the integration and action layer. The distinction helps separate a model error, a source-information error and a failure in a connected service.
What to check before choosing a deployment
Agree on representative Georgian examples, the intended environment and clear acceptance criteria. Test model outputs separately, then evaluate the complete conversation. Review the data-handling requirements before exchanging recordings or documents.
This overview does not substitute for a version-specific model card. Architecture, training data, licensing, model access and measured performance need explicit documentation; ownership alone does not establish those details.
Questions and answers
Does Ertaoza have its own TTS, STT and LLM models?
Yes. Ertaoza has its own models in these three areas. Each has a dedicated page describing its role, relevant use cases and the questions to resolve for a project.
Does “own models” mean trained entirely from scratch?
This website does not make that claim. Model ownership and the technical training approach are different questions. No architecture, training-data size or training-from-scratch specification is published here.
Can I download the models or use a public API?
This section is a model overview, not a download repository or API reference. Contact Ertaoza to discuss the required access and delivery format; do not assume a public endpoint or an open-source licence.
Are accuracy scores and benchmarks available here?
No benchmark numbers are published on these pages. An evaluation should identify the model version, test data, metric and test conditions before making a performance comparison.
Scope and availability
Model roles are described here without fabricated technical specifications, benchmark scores, release versions or licensing terms.
Ertaoza · Updated
Continue with the right next step
Tell us about the task
Describe the language, channel and result you need. Start with non-sensitive examples; agree on access and data handling before sharing recordings or customer information.
Discuss the model your project needs