Vietnamese Speech to Text for Call Centers: Quality Control

01/10/2026

Most call centers I have seen share one habit. At the end of the week a supervisor listens to a handful of random calls, scores them against a checklist, and sends feedback to the agent. A handful out of how many? A tiny fraction of the real volume. The rest sits untouched in a recording archive. That gap is why Vietnamese speech to text deserves serious attention: once a call becomes text, you can search it, count things in it and cross-check it, instead of only listening.

What you get from transcribing calls

The first benefit, and the most obvious, is coverage. Instead of sampling a few calls you hold a transcript of every call, then use rules or a language model to pick out the ones worth a human listen: customers mentioning cancellation, agents skipping the standard greeting, calls that run unusually long.

The second is compliance. Where a script is mandatory, such as reading terms before confirming an order, text lets you check that the sentence was said without replaying every minute. The third is product feedback. The same questions asked again and again are valuable data, and they only show up when you can read thousands of calls at once.

A typical QA flow

• Record the call, and split the two channels (customer and agent) if your PBX allows it.
• Transcribe with a speech recognition model.
• Tag automatically: call topic, sentiment, required phrases.
• Supervisors listen only to flagged calls and compare against the transcript.

The last step matters most. The transcript does not replace the supervisor. It decides which call the supervisor should hear.

Why Vietnamese over the phone is harder than it looks

Contact center audio is difficult data. Traditional phone lines keep a narrow band of frequencies, so a lot of consonant detail is lost. Vietnamese already has pairs that are easy to confuse, like "s" and "x", "ch" and "tr", or the final consonants "n", "ng" and "nh". Over a phone line they get even closer.

Then there are tones. Six tones are a defining feature of the language, and one wrong tone can change the meaning of a whole sentence. When the caller is in a noisy place, or talks over the agent, the model has to lean on context. Add regional accents, local vocabulary, proper names, order codes, phone numbers read digit by digit, and English dropped into the middle of a sentence ("can you check that order for me", said inside a Vietnamese sentence).

So I always tell operations teams not to judge a system by a demo with clean read-aloud speech. Test on your own audio, at peak hours, with your fastest-talking agents. At AIVISION we focus on Vietnamese precisely because of these differences. We have released E1.0, a speech-to-text model for Vietnamese, and our design direction is to favor real-world speech over studio recordings. I am not quoting an accuracy figure here, because such a number only means something when measured on your own data.

Limits worth stating plainly

Automatic transcripts contain errors. That cannot be avoided, and what matters is knowing where the errors live so you can design the process around them. Common trouble spots:

• Numbers and codes: phone numbers, tracking codes and amounts are where mistakes are most frequent and most costly. Add a separate verification step instead of trusting the text blindly.
• Proper names and internal terms: product names, plan names, branch names. Custom vocabularies or finetuning help a lot.
• Overlapping speech: if two people talk at once, even a human listener struggles.
• Emotion: text loses intonation. "Yes, I understand" can be polite or sarcastic, and the words alone will not tell you.

One trap I have watched happen more than once: a QA team scores agents directly from the automatic transcript, and an agent loses points because of a machine error. Trust erodes very quickly. A safer pattern is to use the transcript for suggestions and triage, while a person confirms the score on the calls that matter.

Data and privacy

Calls contain personal information: names, addresses, sometimes account numbers. Before deploying, answer a few practical questions. Where is the audio processed, on your servers or the vendor's? How long is the text retained? Is sensitive information masked before analysis? Are customers told the call is being recorded? These are unglamorous questions, but they decide whether the project clears your legal team.

A small start that works

Do not try to digitize everything at once. Pick one group of calls with a clear script, like order confirmation or complaint handling, take a few hundred, transcribe them, and let supervisors compare the output with what they hear. They will tell you quickly where the system fails and whether it is trustworthy enough to continue. Only then decide whether finetuning on your company's vocabulary is worth it.

If your team is weighing this direction, you can read more about the Vietnamese models AIVISION is building. A good QA system does not start with technology. It starts with what you want to know about your own calls.