E1.0: What AIVISION's Vietnamese Speech to Text Is For

01/10/2026

Have you ever sat down to type up a two-hour meeting from an audio recording? Almost everyone in an office has: rewind, replay the line you missed, and discover the afternoon is gone just to produce the minutes. That is the kind of chore Vietnamese speech to text exists to reduce. E1.0 is the Vietnamese speech-to-text model that AIVISION has released, and this post covers the kinds of work the technology is typically used for, plus the places that call for caution.

What E1.0 is

E1.0 is a Vietnamese speech recognition model: audio goes in, text comes out. Technically this family of problems is called ASR, automatic speech recognition. The 1.0 tells you it is a first version, and we treat it as a starting point rather than a destination.

We will not state an error rate or accuracy figure for E1.0 here. The reason is simple: such a number only means something alongside the test set, recording conditions and scoring method, and we have not published those. If someone quotes a round accuracy without saying what it was measured on, ask them.

What people typically use speech to text for

The scenarios below are typical applications of Vietnamese speech recognition in general. They are examples from the industry, not a customer list or a set of features already deployed in any one product.

Minutes and notes

Meetings, interviews, training sessions, conferences. With a raw transcript afterward, attendees only skim and correct instead of listening again from the start. It is the easiest use to picture, and also where users complain most when names and abbreviations are misrecognized.

Voice data entry

People whose hands are busy, such as warehouse staff, field technicians or drivers, find speaking easier than typing. A note dictated into a phone and turned into text can save considerable time compared with typing on a small screen.

Subtitles and search in video and podcasts

Text alongside audio makes content easier to search, easier to subtitle and more accessible to people with hearing loss. An archive of thousands of hours of video without text is nearly impossible to look things up in.

Contact centers

Calls are converted to text for summarizing, classifying or quality review. This step often pairs with an LLM that reads the transcript. It is also where speech to text and language models meet, which is why AIVISION works on both.

Healthcare and pharma, in an administrative role

In healthcare, speech to text can capture dictation so a qualified person can review and edit it, for instance drafting administrative notes. The limits need to be clear: the tool only supports information and paperwork. It does not diagnose, prescribe or advise on dosage, and medical staff must check everything. A single misrecognized word in a drug name can have real consequences, so review by a qualified person is mandatory, not optional.

What makes a transcript usable

From building products, we see that users do not judge a model by a table of numbers. They judge by feel: do I have to fix too much? If fixing takes longer than retyping, the tool gets dropped. If it is only a few corrections, it becomes a habit.

Several factors drive that feel:

• Input audio quality. A good microphone in a quiet room gives a very different result from a speakerphone recording in a noisy cafe.
• Punctuation and capitalization. A solid block of text without sentence breaks is hard to read even if every word is right.
• Names and terminology. A classic weak spot, covered in more detail in the next post in this series.
• How people speak. Fast speech, mumbling or several people talking over each other makes life hard for any system.

Using it sensibly

Our most practical advice is to treat speech-to-text output as a draft. A good draft saves a great deal of time, but wherever errors carry consequences, such as contracts, medicine or legal matters, have a person read it again.

If you plan to integrate it into a workflow, test with your own real data first: the voices of people in your organization, the microphones you actually use, and the vocabulary of your field. A demo on a clean broadcast voice rarely reflects that.

Worth adding that E1.0 is only one link in a chain. The larger value usually comes when the text feeds the next step: summarizing, extracting key points, filling forms. That is why, alongside the speech model, we develop the L1.0 Vietnamese LLM and do finetuning for specialist domains.

The product team at AIVISION keeps improving, and feedback from real users is worth more than any lab. For markets with other languages, like Mexico or the Philippines, speech recognition shares the same problem frame, even though the linguistic details differ entirely.