Handling Multilingual (VI-EN) Conversations in Voicebots: Real-Time Translation Techniques

In international business environments, language barriers often disrupt the customer experience. Building a multilingual voicebot is not just about translating sentences in isolation; it requires tight synchronization between speech recognition (STT), large language models (LLM), and speech synthesis (TTS). This article analyzes the core techniques for handling voice translation between Vietnamese and English in real-time, helping businesses optimize their automation workflows.

The Challenges of Multilingual Conversations in Voicebots

Unlike text, speech carries intonation, speaking speed, and non-verbal cues. When users switch between Vietnamese and English (code-switching) within a single sentence, standard systems often struggle with recognition errors or context loss.

Consider a real-world example: A customer says, "I need to book a flight to New York for next week." If the system only processes Vietnamese, it might misinterpret "New York" or miss key terms. If it processes pure English, it fails to recognize Vietnamese sentence structures.

To solve this, the processing pipeline must be designed around three main stages:

1. STT (Speech-to-Text): Multilingual speech recognition.
2. LLM (Large Language Model): Context understanding and semantic translation.
3. TTS (Text-to-Speech): Natural speech synthesis.

Technique 1: STT Supporting Code-Switching

The most critical element is an STT model smart enough to handle language interleaving. AIVISION has developed STT models focused on Vietnamese but capable of accurately processing interspersed English words, a common feature in communication in Vietnam.

Why Monolingual STT Is Not Enough

Monolingual STT models often struggle with foreign keywords. They may:

• Ignore English words.
• Misrecognize terms due to the lack of a second-language dictionary.
• Lose word timestamps, making synchronization with TTS difficult.

AIVISION provides STT services with code-switching capabilities, ensuring that English words within Vietnamese sentences (or vice versa) are recognized accurately. This forms the foundation for precise voice translation.

Technique 2: LLM as the Semantic Bridge

After receiving text from STT, the LLM acts as the "brain" of the voicebot. The LLM's role is not just to translate text, but to understand the user's intent and generate responses appropriate to the target language context.

Handling Multilingual Context

Assume the user speaks Vietnamese, but the voicebot is configured to respond in English for tourists.

• Input (STT): "I want to change my ticket to tomorrow." (Vietnamese input)
• LLM Processing: Identifies the intent as "change_flight_date." Translates the meaning to English: "I would like to change my flight to tomorrow."
• Output (TTS): "Certainly, I can help you change your flight to tomorrow. Please confirm the new date."

AIVISION's LLM (aivision-L1.0) supports both Vietnamese and English, allowing for flexible handling of multilingual conversations. Thanks to streaming capabilities, the LLM can begin generating the response as soon as it receives the beginning of the query, minimizing overall latency.

Technique 3: Natural and Synchronized TTS

Finally, text is converted into speech via TTS. For a smooth experience, TTS needs to:

• Accurately read English words in Vietnamese sentences: For example, the word "AI" in "AI technology is developing" should be read as "A-I" or "Eye," depending on context, rather than attempting a Vietnamese phonetic approximation.
• Provide natural voices: Avoid robotic tones. AIVISION's TTS (aiv-tts-S.1.0) offers natural voices for both Vietnamese and English, supporting telephony formats like G.711 for call centers.

Comparison of Voicebot Pipeline Components

Component	Function	Key Multilingual Factor
STT	Converts speech to text	Code-switching recognition (VI-EN), word timestamps
LLM	Processes logic, translates meaning, generates responses	Multilingual context understanding, streaming support
TTS	Converts text to speech	Accurate foreign word pronunciation, natural voices, telephony support

Practical Deployment Tips

To build an effective multilingual voicebot, consider the following:

• Use Streaming APIs: Instead of waiting for the entire question to be recorded before processing, use WebSocket for STT and LLM. This significantly reduces latency, creating a more natural conversational feel.
• Handle Recognition Errors: Always include a mechanism to verbally confirm important information (such as names or dates).
• Optimize Dictionaries: If your domain has many English technical terms, ensure the STT and LLM are configured to prioritize these keywords.

AIVISION has supported hundreds of enterprises in Vietnam and abroad in deploying speech AI solutions. With practical experience, we understand that the quality of voice translation depends on the seamless integration of STT, LLM, and TTS components.

Conclusion

Building a multilingual voicebot is no longer an insurmountable challenge thanks to the development of specialized AI models. By combining code-switching STT, multilingual LLMs, and natural TTS, businesses can create smooth conversational systems, minimize errors, and enhance the customer experience.

If you are looking for an integrated STT TTS LLM solution capable of handling Vietnamese and English, consider AIVISION's services. We provide easy-to-use APIs, streaming support, and telephony formats to help you deploy quickly.

Contact our technical team for detailed consultation on your specific needs, or Start free today to experience AIVISION's speech AI quality. Explore more knowledge-sharing articles on our Blog.

Frequently asked questions

Can a multilingual voicebot handle switching between Vietnamese and English within a single sentence?

Yes, if the system uses an STT model that supports code-switching. AIVISION's STT is designed to accurately recognize interspersed English words in Vietnamese sentences and vice versa, ensuring semantic accuracy.

What factors influence latency in real-time voice translation?

Latency depends on the processing speed of STT, LLM, and TTS. Using streaming APIs (such as WebSocket for STT and LLM) significantly reduces latency compared to processing by file or complete sentence.

Does AIVISION support voice formats for call centers?

Yes, AIVISION's TTS supports common telephony formats such as 8 kHz G.711 μ-law and A-law, suitable for integration into telephone systems and call centers.

How can I improve STT accuracy for English technical terms?

You can configure a domain-specific dictionary. AIVISION's STT handles English words in Vietnamese contexts well, but optimizing the domain dictionary will further increase accuracy for specific terms.

Do I need separate STT models for Vietnamese and English?

Not necessarily. Modern models, including AIVISION's solutions, can handle multilingual or code-switching in a single pipeline, simplifying system architecture and reducing deployment complexity.

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