Meeting Minutes with Vietnamese Speech to Text: What Works

01/10/2026

Anyone who has taken minutes knows the feeling: listening, typing, and worrying that you missed the one decision that mattered, only to end up with notes that are a pile of fragments. Using Vietnamese speech to text to produce meeting minutes sounds like a clean answer to that pain. It solves part of it. The other part deserves plain talk, because every tool has things it does not do well yet.

What the machine handles

The first job is transcription. A one-hour meeting becomes a full, searchable text. You remember someone mentioning the report deadline but not when? Type a keyword. That alone changes how many teams work, because it ends the "but you said so in that meeting" arguments.

The second job is using a language model to condense the transcript into minutes: key topics, decisions made, action items with owners, open questions. This is where a Vietnamese LLM earns its place, because it must understand how a meeting is structured rather than just collect words. Pairing speech to text with an LLM is the sensible architecture, and it is the direction AIVISION cares about, since we have both E1.0, a speech-to-text model, and L1.0, an LLM, for Vietnamese.

What good minutes contain

General info: time and attendees.
Decisions, one per line, with a clear subject.
Action items: who, what, by when.
Unresolved issues for the next meeting.

A machine can build this skeleton easily. What goes inside the skeleton still needs a human check.

Where it still snags

I once fed a real internal meeting with six participants into a transcription system. The result was readable but showed some very typical errors. The group often talked over each other when the discussion got lively, which is exactly when the model gets confused. Attribution of who said what gets mixed up, yet minutes need the right person.

Speaker diarization remains a hard problem, especially when everyone sits in one room around a single microphone. If each person has their own headset you get separate channels and the problem eases a lot. A large meeting room with one mic in the middle of the table is different: echo, quiet speakers far from the mic, fans, keyboard clatter.

Then there is language mixing. Workplace meetings in Vietnam blend Vietnamese and English constantly, from single words like "deadline", "KPI" and "pipeline" to whole sentences. Add internal abbreviations, project names, client names. A general model often misspells these, and the LLM downstream tries to "guess something plausible", sometimes producing a fluent sentence nobody actually said.

The biggest risk: summaries that sound right but are wrong

This is the risk that worries me most. LLM summaries tend to be smooth, and the smoothness lowers the reader's guard. A decision to "postpone to next week" can become "cancelled". A task given to one colleague can be attached to another because both were named in the same passage. Nobody means to be wrong, but wrong minutes have real consequences.

So the workflow should have two safeguards. First, the minutes always link back to the original transcript segment, so anyone in doubt can click and listen. Second, commitments such as decisions, deadlines and figures must be confirmed by the chair before the minutes go out. It costs a few extra minutes and saves a whole follow-up meeting spent correcting a misunderstanding.

On confidentiality

A board meeting or an HR discussion is not something you want to send to an outside service without knowing where it is stored. Ask clearly about where data is processed, how long it is kept, and whether it is used for retraining. For many organizations, on-premise deployment is a requirement, and system design should account for that from the start.

Using it well

In my experience the teams that get value do a few simple things. They say their name the first time they speak, so speakers are easier to attach. They use decent microphones, since input audio quality decides most of the outcome. They prepare a list of terms and project names. And they treat the machine output as a first draft, not the final version.

If you want to see how AIVISION approaches Vietnamese, from speech recognition to language models, the website is the place to look. As for minutes: let the machine do the transcribing and the framing, and let people do the confirming. With that split, each side works where it is strongest.