Georgian TTS
Turn reviewed Georgian text into spoken content and voice-assistant replies.
Explore this directionTechnology
Georgian speech technology: STT, LLM and TTS
In short: Ertaoza’s speech pipeline has three distinct model roles: STT converts speech to text, the LLM processes language and context, and TTS converts text to speech. For teams building a voice workflow, each stage needs its own evaluation as well as an end-to-end conversation test.
How do STT, LLM and TTS work together?
STT is speech recognition: it produces a transcript. An LLM is a language model that processes text and context. TTS is speech synthesis: it produces spoken audio from text. A separate integration layer governs any business action; generating a sentence does not prove an action succeeded.
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Voice assistants, agents and our own AI models
Separate pages explain conversation, business actions and the model behind each speech or language task.
Georgian STT
Speech recognition for Georgian transcripts and the input to a spoken conversation.
Explore this directionGeorgian LLM
Language processing between a person’s request, approved context and a useful response.
Explore this direction01–03
Three parts of one conversation
Each layer has a different job. Together they make an interaction more understandable and easier to review.
Georgian language
Why Georgian context matters
Georgian has its own morphology, word order, and shifts in meaning. Recognising words is not enough: a system needs to understand the purpose of a phrase, its setting, and when a person should take over.
Which voice AI component matches your task?
Compare the input and desired result before choosing a component. Speech recognition, language processing and speech synthesis solve different problems; a conversational workflow also needs information sources, permissions and a path to a person.
| Task | Starting point | What must be checked |
|---|---|---|
| Turn a recording into text | STT — speech recognition | Transcript fidelity, names, recording conditions and review. |
| Turn written content into spoken audio | TTS — text-to-speech | Pronunciation, text preparation and listening quality. |
| Process a question with approved context | LLM — language model | Answer grounding, uncertainty and the task boundary. |
| Help a person through a conversation | AI voice assistant | The information source, dialogue and human handoff. |
| Request a business-system action | AI voice agent | Permission, confirmation and a verified system result. |
Frequently asked questions
Which model do I need for audio transcription?
Start with STT when the required output is text from a recording or a spoken request. Then decide whether the transcript needs human review, punctuation, terminology handling or further language processing. TTS does the opposite task and is not a transcription model.
Can one model score describe the whole conversation?
No. Transcription errors, an unsuitable answer, mispronunciation and a failed tool call are different failures. Evaluate each layer and the complete workflow on relevant examples. These pages do not publish a combined accuracy claim or a benchmark result.
What should I verify before integrating a model?
Confirm the actual access method, supported inputs, output format, data handling and licensing with Ertaoza. The model pages explain purpose and evaluation questions; they do not promise public API endpoints, downloadable weights or a specific hosting arrangement.
Continue with the relevant guide
Sources and context
The model and service descriptions are Ertaoza’s own information. The following original media sources document historical public context, not an independent performance benchmark.
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