How to Measure Vietnamese Speech Recognition Accuracy: WER, CER and the Traps

In the field of artificial intelligence, evaluating speech recognition accuracy is far more complex than simply comparing a single number. Many businesses fall into a trap by looking only at a generic metric while ignoring the practical context. This article helps you understand Word Error Rate (WER) and Character Error Rate (CER), while highlighting common mistakes when testing Automatic Speech Recognition (ASR) systems for Vietnamese.

Why WER and CER Matter

When deploying a Speech-to-Text system, you need an objective metric to compare vendors or model versions.

• WER (Word Error Rate): This is the most common metric. It calculates the number of words deleted, inserted, or substituted relative to the ground truth text. A lower WER indicates higher accuracy.
• CER (Character Error Rate): This metric is useful for languages with discrete character-based writing systems or when absolute precision at the character level is required, particularly in medical or legal applications.

For Vietnamese, due to its specific orthography and tonal marks, WER is typically the preferred metric in industry-standard reports. However, WER is not the whole story. A system with a 10% WER might still be "unusable" if those errors occur in critical keywords such as proper nouns, numerical data, or domain-specific terminology.

The "Traps" When Measuring Vietnamese Accuracy

Many technical teams struggle when lab benchmark results differ significantly from real-world performance. Here are the most common pitfalls:

1. Ignoring Text Normalization

Vietnamese raw data often contains inconsistent writing styles. Before calculating WER, you must normalize both the predicted text (hypothesis) and the reference text.

• Punctuation: Commas, periods, and question marks are often not predicted accurately by ASR models and do not significantly affect meaning. Keeping them in the WER calculation introduces artificial errors.
• Numbers and Units: Converting "one million" to "1,000,000" or vice versa. If the system reads "twelve" and you record it as "12", the comparison system must recognize these as equivalent.
• Typos and Variations: Vietnamese data often contains typing errors or tonal variations. You need rules to handle extra characters or equivalent substitutions.

Without normalization, your WER could be 5–10% higher than reality, leading to an incorrect assessment of the model’s capability.

2. Non-Representative Test Data

Using a small test set (e.g., 1 hour of audio) or data from a single source (e.g., only male voices, only recorded in a quiet room) leads to artificially optimistic results.

• Voice Diversity: Vietnamese has significant phonetic differences between regions (North, Central, South). A good model must perform stably across all three regions.
• Background Noise: In enterprise environments, noise from fans or traffic is common. Testing in an anechoic chamber does not reflect real-world speech recognition accuracy.

3. Ignoring Code-Switching

In Vietnam, users frequently mix English into Vietnamese sentences (e.g., "I need to send this email to the client"). Many traditional Vietnamese ASR models handle these English words poorly, causing WER to spike.

A modern system must recognize and process both Vietnamese and English within the same sentence. If you are building a solution for a multinational enterprise, ensure your test data contains at least 20–30% of sentences with mixed English.

Comparison of Measurement Metrics

Metric	Basic Formula	Pros	Cons	When to Use
WER	(S + D + I) / N	Common, easy for international comparison	Sensitive to keyword errors, hard to distinguish typo types	General evaluation, vendor comparison
CER	(S + D + I) / M	Precise at character level	Sensitive to tonal marks, less common for Vietnamese	Applications requiring absolute character precision
SER	Sentences with errors / Total sentences	Reflects user experience (correct/incorrect sentence)	Overly sensitive; one small error fails the whole sentence	Evaluating chatbot quality

Practical Advice from Experts

To obtain reliable Vietnamese WER figures, follow this process:

1. Build a Held-Out Test Set: Do not use training data for testing. The dataset should contain at least 5–10 hours of audio, including diverse speakers and noise levels.
2. Automate Normalization: Write Python scripts to normalize text before running WER tools. Ensure this process is applied consistently to every model you compare.
3. Evaluate by Context: Don’t just look at the average WER. Analyze errors by type: proper nouns, numbers, and technical terms. These are the errors that cause the most serious business consequences.
4. Test on Real Devices: If your product runs on mobile or headsets, test on these devices. Microphone quality significantly affects input, and consequently, ASR output.

AIVISION and Measurement Standards

At AIVISION, we understand these challenges deeply. We have built internal test sets that are rigorously normalized, including medical data (ViMedCSS) and real business meeting recordings. Our benchmarks show that AIVISION’s models achieve an average WER of 11.84%.

Crucially, we do not just optimize for low WER; we ensure that mixed English terms are recognized accurately, meeting the needs of enterprises operating in Vietnam and internationally.

If you are looking for an accurate Speech-to-Text solution optimized for Vietnamese with code-switching capabilities, try it now. You can Start free to verify accuracy on your own data.

Summary

Measuring speech recognition accuracy is a complex technical process that cannot rely on a single WER number. Pay attention to text normalization, ensure test data diversity, and evaluate errors in a real-world context. Avoiding these "traps" will help you choose the most suitable AI solution for your product.

To learn more about our other AI technologies, check out our Pricing or contact AIVISION’s technical team directly via Contact.

Frequently asked questions

What is an acceptable WER for Vietnamese?

A WER below 10–15% is generally considered good for commercial applications. However, the acceptable threshold depends on the specific field. In medical or legal contexts, requirements may be stricter, while entertainment applications may allow for more flexibility.

What is the difference between WER and CER?

WER (Word Error Rate) calculates errors based on the number of words, while CER (Character Error Rate) calculates errors based on the number of characters. WER is more common in Vietnamese because it better reflects changes in word meaning. CER is useful when absolute precision at the character level is needed.

How can I improve ASR accuracy for regional accents?

You need to train the model on diverse data, including voices from different regions (North, Central, South). Using accurately annotated and contextually diverse data helps the model generalize better.

How does code-switching affect WER?

If a model is not well-trained for code-switching (mixing English), WER will increase due to English words being misrecognized or omitted. A modern ASR system must handle both languages smoothly to maintain a low WER.

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