Vietnamese-English Code-Switching: The Biggest Speech-to-Text Challenge

In the modern business environment, Vietnamese employees frequently interweave English into their daily conversations, from product names and technical terms to management concepts. This phenomenon, known as code-switching, is widely considered the biggest challenge for traditional speech-to-text (ASR) systems. If not handled correctly, Vietnamese speech mixed with English leads to missing words, spelling errors, or misinterpretations, resulting in high editing costs and reduced efficiency in automation workflows.

This article explores the technical nature of code-switching, why it is so difficult for AI models, and how businesses can overcome these hurdles to achieve high accuracy in meetings, conference recordings, and customer care interactions.

Why Is Code-Switching So "Difficult" for AI?

Unlike language translation, code-switching occurs within the same sentence, sometimes even between words. For humans, context and background knowledge allow us to immediately understand that "Deadline" means a due date or "KPI" refers to performance metrics. However, for machines, this is a complex problem involving acoustics and linguistics.

Phonetic and Phonological Barriers

Vietnamese and English have vastly different phonological systems. When Vietnamese speakers pronounce English words, they often apply Vietnamese phonetic rules. For example, the word "System" might be pronounced similarly to "Si-tem" or "Xy-tem." Speech-to-text models trained primarily on pure Vietnamese data often fail to recognize these acoustic variants, leading to transcriptions that sound similar in Vietnamese but carry unrelated meanings.

Lack of Mixed Training Data

Most public datasets for Vietnamese consist of standard read text, lacking natural conversations with interspersed English. This scarcity of "mixed" data prevents models from learning the rules of language switching. Consequently, when encountering an English word, the model may either ignore it or guess randomly, increasing the Word Error Rate (WER).

Real-World Impact on Businesses

For technology, finance, or import/export companies, recording and transcribing speech is an urgent need. However, if the speech-to-text system does not handle code-switching well, meeting minutes become meaningless or require extensive manual correction.

• Loss of Critical Information: Technical terms in English (such as API, Blockchain, ESG) may be transcribed as nonsensical Vietnamese words.
• Reduced Productivity: Administrative staff or assistants must spend hours each day reviewing and correcting automated text.
• Data Analysis Bias: Sentiment analysis or Named Entity Recognition (NER) systems perform poorly if the input text contains spelling errors caused by language mixing.

The Solution: Models Trained Specifically for the Vietnamese Market

To solve this problem, the solution does not lie in using a generic multilingual model, but in utilizing a model specifically optimized for Vietnamese language characteristics and local user code-switching habits.

AIVISION has developed speech-to-text models trained on thousands of hours of real-world Vietnamese data, including conversations with interspersed English.

Độ chính xác cao

According to AIVISION's internal benchmarks, their speech-to-text model achieves an average Word Error Rate (WER) of 11.84%. This means AIVISION's model produces a low error rate in transcription tasks.

Notably, in the ViMedCSS dataset (medical domain with many English terms), the model maintains high accuracy, demonstrating its ability to handle specialized English vocabulary within a Vietnamese context effectively.

| :--- | :---: | :---: | | FLEURS-vi Set | 4.58% | Significantly Higher | | VIVOS Set | 6.83% | Significantly Higher |

Multilingual Support and Translation

Beyond Vietnamese, the AIVISION system supports 24 other languages, including English, Spanish, Thai, Filipino, Chinese, Japanese, Korean, and Indonesian. This is particularly useful for businesses with cross-border operations, where code-switching may extend beyond Vietnamese-English to include other languages.

Deployment Advice for Businesses

To optimize the speech-to-text experience in code-switching environments, businesses should adopt the following strategies:

1. Standardize Vocabulary (Glossary): Provide a list of specialized English terms that employees frequently use. Many modern speech-to-text APIs, including AIVISION’s, allow users to configure custom glossaries to improve accuracy.
2. High-Quality Input: Use high-quality microphones and reduce background noise. Nhiễu là thách thức lớn đối với ASR, và trở nên quan trọng hơn khi mô hình phải xử lý chuyển đổi ngôn ngữ.
3. Combine with LLMs: After obtaining raw text from speech-to-text, use a large language model (LLM) like aivision-L1.0 to review grammar and semantics. LLMs can understand context and fix minor errors caused by ASR, especially mispronounced English words.

Conclusion

Code-switching is no longer a rare phenomenon; it has become the standard in business communication in Vietnam. Choosing a speech-to-text solution that handles this phenomenon well is a key factor in effectively automating text workflows.

With experience deploying speech AI for hundreds of enterprises in Vietnam and abroad, AIVISION is committed to providing accurate, stable, and optimized speech recognition for the Vietnamese market. If you are looking for a speech-to-text tool that handles Vietnamese mixed with English effectively, experience our service today.

You can start with a free trial or review the Pricing details to choose the package that best fits your business needs. For specific technical consultation, please Contact the AIVISION support team.

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Frequently asked questions

What is code-switching in natural language processing?

Code-switching is the phenomenon where speakers switch between two or more languages within the same sentence or conversation, most commonly Vietnamese mixed with English in office environments.

Why does speech-to-text often make mistakes when encountering English words in Vietnamese sentences?

This is because AI models are typically trained on pure Vietnamese data, so they fail to recognize the variant pronunciations Vietnamese speakers use for English words, leading to transcription errors.

Does AIVISION support speech recognition for other languages?

Yes, in addition to Vietnamese, AIVISION supports 24 other languages, including English, Spanish, Thai, Filipino, Chinese, Japanese, Korean, and Indonesian.

How can I improve speech-to-text accuracy for specialized terminology?

You should use the glossary configuration feature in the API to provide the system with a specific list of specialized English terms.

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