Preparing Audio Data: Cleaning and Labeling Workflow for Vietnamese Speech-to-Text

Building an accurate Speech-to-Text (STT) system for Vietnamese relies on more than just advanced algorithms. The most critical factor is the quality of the input data. Many AI engineers overlook the preparation of AI data, which can lead to models struggling with real-world scenarios like background noise, regional accents, or language mixing. This article explores a professional workflow for processing speech-to-text data, from cleaning audio signals to effective speech data labeling techniques, helping you optimize your model's performance.

The Importance of High-Quality Datasets

In natural language processing, particularly for audio, data is the fuel for the model. For Vietnamese, the challenge lies in its complex tonal system and diverse regional accents. If input data is noisy or mislabeled, the model will learn these errors and amplify them during inference.

AIVISION, with experience deploying speech AI for hundreds of enterprises in Vietnam and countries such as the USA, Mexico, the Philippines, and Thailand, has assembled a Vietnamese corpus of 690,517 hours. However, our Speech-to-Text model was trained on only 9,043 hours of carefully curated data from this pool. This demonstrates that quality always matters more than quantity. A small, clean, and diverse dataset yields better results than a large dataset filled with noise.

5-Step Workflow for Processing Speech-to-Text Data

To ensure data meets high standards, you must follow a systematic process. Here are the core steps in the speech-to-text data processing workflow:

1. Collection and Classification of Raw Data

Audio data can come from various sources: call center recordings, meeting notes, podcasts, or speech in noisy environments. The first step is to classify data by context. For example, telephone speech (typically 8kHz) is technically different from high-quality recordings (16kHz or 44.1kHz). You need to identify the source clearly to apply the appropriate filters.

2. Audio Signal Cleaning

This is the most important step for removing noise factors. Common techniques include:

• Noise Reduction: Removes background sounds such as air conditioning or traffic.
• Amplitude Normalization: Ensures consistent volume across files, preventing files that are too quiet or too loud.
• De-reverberation: Reduces echo effects in enclosed spaces, allowing the model to focus on the primary voice.

Note: Avoid over-cleaning, as it may remove biological voice characteristics, making it difficult for the model to distinguish subtle tones.

3. Time Stamping

Each utterance needs to be associated with precise time markers (word timestamps). This helps the model understand sentence structure and supports more accurate labeling. In practical applications like meeting notes, knowing exactly which word was spoken at which second is key to creating structured transcripts.

4. Speech Data Labeling

Speech data labeling is the step that converts audio signals into text. For Vietnamese, this process requires labelers to have good comprehension of dialects and handle code-switching (mixing Vietnamese and English).

• Transcription Labeling: Records the content exactly as spoken.
• Normalized Labeling: Corrects spelling errors, adds punctuation, and standardizes abbreviations.

Practical Tip: Use a two-tier labeling process. The first tier is a quick pass, and the second tier involves cross-checking by another person or using AI assistance to detect inconsistencies.

5. Quality Assurance and Error Removal

After labeling, a final Quality Assurance (QA) step is essential. Remove files with high error rates, long silences, or truncated segments. Ensure that the labeled text matches the audio content perfectly.

Comparison of Labeling Methods

Method	Advantages	Disadvantages	Suitable Application
Manual Labeling	Highest accuracy	Time-consuming, higher labor cost	Core Training Data
Semi-Automatic	Faster, lower cost	Requires review, prone to missed errors	Data Augmentation
Automatic (AI)	Extremely fast	Prone to confusion with noise	Raw Data Preprocessing

Tips for Preparing Vietnamese AI Data

When working with Vietnamese data, pay special attention to the following factors:

1. Diversify Speakers: Ensure the dataset includes male, female, young, and old voices, as well as different regional accents. A model trained only on Hanoi accents will struggle with Saigon or Central Vietnamese accents.
2. Handle Code-Switching: In business environments, mixing in English terms is very common. Data should reflect this reality, e.g., "I will send an email to the client before 5 PM."
3. Domain Context: If the model serves medical or financial sectors, include domain-specific terminology in the labeling vocabulary. AIVISION has demonstrated the effectiveness of this approach, achieving good accuracy on the ViMedCSS test set (medical, with English terms) with an average Word Error Rate (WER) of 15.68%.

Conclusion

The workflow for preparing AI data for Vietnamese Speech-to-Text is meticulous, requiring a combination of signal processing technology and linguistic knowledge. Investing time in the cleaning and speech data labeling stages is directly proportional to the final model's accuracy.

If you are looking for a Vietnamese Speech-to-Text solution optimized with high-quality data, try AIVISION. We provide real-time recognition and file processing APIs, supporting Vietnamese-English code-switching with proven accuracy across various fields.

View service packages here or contact our technical team for consultation on your data needs.

Frequently asked questions

Why does Vietnamese data require more thorough cleaning than English?

Vietnamese has six tones and a complex consonant system, making small audio errors (like noise) likely to cause tonal confusion. Additionally, dialect diversity requires careful filtering to prevent the model from learning incorrect regional features.

How many hours of data are needed to train a Vietnamese STT model?

There is no fixed number, but quality matters more than quantity. AIVISION used 9,043 hours of curated data from a 690,517-hour pool to train its model, showing that optimizing a small but clean dataset is an effective strategy.

How should mixed Vietnamese and English cases be handled in data?

Ensure labelers have good comprehension of both languages. During labeling, keep English words as they are pronounced by native Vietnamese speakers, rather than forcing standard English pronunciation, so the model learns the actual usage.

Should synthetic data be used to supplement Vietnamese datasets?

It can be used, but with caution. Synthetic data increases volume but may lack natural characteristics like breathing or natural pauses. It should be used as a small supplement (less than 20%) and thoroughly checked for quality before training.

How can you evaluate dataset quality before training?

You can calculate the labeling error rate (WER) on a small subset (test set) labeled by experts. If the WER on this test set is low (e.g., under 5% for read speech), the dataset is likely high quality. Also, check for even distribution of voices and contexts.

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