Automating Vietnamese-English Meeting Translation: STT and LLM APIs

In international business environments, the language barrier between Vietnamese and English remains a significant challenge, causing many organizations to lose valuable time to manual translation. Implementing AI meeting translation not only bridges this gap but also enhances operational efficiency through immediate processing. This article analyzes how to combine Speech-to-Text (STT) and Large Language Model (LLM) APIs to build an automated system, resulting in accurate and professional multilingual meeting minutes.

Why Automate Meeting Translation?

Bilingual meetings typically impose two strict requirements: accuracy in domain-specific content and real-time response speed. When relying on human interpreters, costs and waiting times are unavoidable. In contrast, a well-designed automated system solves this by converting speech to text and translating meaning instantly.

The core benefits of adopting this technology include:

• Reducing Errors: Eliminates mishearing caused by audio quality or context, particularly with technical terms.
• Accelerating Processing: Text is generated almost simultaneously with speech, allowing participants to grasp information in real-time.
• Easy Storage and Querying: Text-based minutes are searchable, analyzable, and archivable for the long term.

Technical Workflow: Combining STT and LLM APIs

To build an effective Vietnamese-English translation API system, you must understand the two main components: Speech-to-Text (STT) technology and Large Language Models (LLMs). Below is the general architecture of this automation process.

Step 1: Real-Time Speech Recognition (Real-time STT)

This is the first and most critical step. Audio data from the meeting (via microphone or transmission line) is sent to the STT service. A robust system must support streaming over WebSocket to ensure low latency.

When selecting an STT provider, pay close attention to its ability to handle Vietnamese and English code-switching, a common characteristic in Vietnamese business meetings. AIVISION provides an STT solution focused on Vietnamese, supporting both real-time streaming and file transcription, with strong performance in conversations containing both languages.

Step 2: Pre-processing and Sentence Segmentation

Raw text from STT often contains filler words, minor spelling errors, or run-on sentences. Before feeding it into the LLM, you should perform pre-processing steps:

1. Remove unnecessary filler words (such as "uh," "um," or "hmm").
2. Divide the text into reasonable sentences or small paragraphs to optimize token input for the LLM.
3. Add context if the STT system has low confidence in certain industry-specific keywords.

Step 3: Translation and Synthesis via LLM

This is where artificial intelligence enhances the capabilities of traditional machine translation tools. The LLM does not just translate word-for-word; it understands context, tone, and the speaker's intent.

When designing the prompt for the LLM, you must clearly specify the role:

"You are a professional interpreter for business meetings. Translate the following Vietnamese text into English, maintaining a formal tone and ensuring the accuracy of technical terms."

Using an LLM ensures that the translation is not only grammatically correct but also natural and aligned with the communication culture of native speakers.

Practical Example: Application in Business

Consider a specific scenario: A meeting between a Vietnamese engineering team and a US partner regarding a software project.

1. Recording: The system records the meeting via WebSocket API.
2. Transcription: The STT API converts speech into Vietnamese text (which may be mixed with English).
3. Processing: The LLM receives the text and performs two tasks:

• Translate it into English for display to the US partner.
• Summarize action items and key decisions.

4. Publication: The system generates a multilingual meeting minute with two columns: Vietnamese and English, accompanied by a to-do list.

This process ensures both parties grasp information fairly and accurately, minimizing misunderstandings caused by language barriers.

Choosing the Right Technology

While the market offers many solutions, not every service is optimized for Vietnamese. When evaluating options, consider the following criteria:

Criterion	Technical Requirement	Note
STT Accuracy	Good support for Vietnamese and code-switching	Test accuracy on real-world data
Latency	Real-time streaming support	Critical for live meetings
Customization	Ability to customize LLM prompts	Ensures tone fits the industry
Security	Data encryption, regulatory compliance	Especially important for enterprise data

You can explore applications like Live Translate or Multilingual Meeting rooms on the s2speech platform to see how this technology is being applied in practice.

Implementation Tips

To achieve the highest efficiency when automating translation, you should:

• Test on Real-World Data: Do not rely solely on sample data. Run trials with internal meetings to evaluate STT accuracy and LLM translation quality.
• Customize Industry Vocabulary: Provide a specific glossary of keywords to the LLM to ensure internal terms are translated consistently.
• Monitor and Improve Continuously: Collect user feedback on translation quality and adjust prompts or system parameters over time.

Combining STT and LLM APIs is a significant step forward in enhancing multilingual work efficiency. Instead of spending time on manual translation, businesses can focus on the core content of the meeting.

Conclusion

Automating Vietnamese-English meeting translation is no longer a distant concept but a feasible and effective solution thanks to AI advancements. By combining accurate speech recognition technology with intelligent large language models, you can create multilingual meeting minutes quickly and professionally.

If you are looking for a reliable AI meeting translation solution, try AIVISION's tools. The s2speech platform offers flexible APIs, supporting both Vietnamese and English, helping you easily build your own translation system for your business.

Our Pricing is transparent, with per-token fees and free for every account. Start now to explore the potential of speech AI in your business operations.

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Frequently asked questions

How do STT and LLM APIs differ in the translation workflow?

STT is responsible for converting speech into raw text, while the LLM handles understanding context and translating that text into the target language. Both must work closely together to achieve high quality.

Can this technology be used for live meetings?

Yes, thanks to real-time streaming capabilities over WebSocket, the system can display subtitles or translations while the meeting is in progress, allowing participants to follow content instantly.

How can I ensure the accuracy of industry-specific terms?

You can provide a glossary of industry terms to the LLM in the prompt, instructing the system to prioritize the most accurate terminology for your field.

Does AIVISION support languages other than Vietnamese and English?

Yes, the s2speech platform supports 24 additional languages for translation applications, including Spanish, Thai, Chinese, Japanese, Korean, Indonesian, and many others.

How is the cost of using these APIs calculated?

New users is free to use to experience the service.

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