Turn Thai audio
داخل words that hold up.

A different approach to Thai transcription.

Thai is spoken by more than 60 million people, almost entirely within Thailand, and it carries five tones, mid, low, falling, high, and rising, that change a word’s meaning entirely. Standard written Thai also drops spaces between words inside a sentence, using spacing only to mark the end of a clause or a sentence. Turning Thai speech into usable text has always meant either a native transcriber working by hand, or software that gets the syllables roughly right and drops the tone, the segmentation, and everything that depended on them.

Where Thai transcription usually breaks down

Most transcription tools are tuned for languages with clear word boundaries and no tone system, and Thai has neither. A tool that segments word by word instead of reading the sentence as a whole produces text that is technically transcribed and functionally unreadable, with words run together or split in the wrong place. Regional speech, Isan in the northeast, Kham Mueang in the north, Pak Tai in the south, adds vocabulary and pronunciation that a Central Thai-only model never learned. Teams end up correcting entire passages by hand, which erases most of the time an automated tool was supposed to save.

Reading the recording, not just the words

Speak AI transcribes Thai audio and video with speaker labels and timestamps, then reads the recording itself: who is speaking, where the tone or emphasis shifts, and when a speaker switches between Thai and English mid-sentence. Names, terms, and callback details are extracted into structured fields your team can search and query. Then you can ask across your entire Thai archive with AI chat, using ChatGPT, Claude, and Gemini built directly into the workspace.

• Thai audio and video, transcribed with speaker labels and translation into English or another language.
• Full utterances read in context, not segmented word by word, so tone and meaning stay intact.
• Captions and subtitles (SRT, VTT) generated straight from the timestamped transcript.
• Export to Word, PDF, TXT, CSV, or JSON, depending on how your team needs to use it.

From one file to a searchable archive

The result is a Thai archive that behaves like a well-run English one. Interviews, hearings, and support calls become searchable text instead of a folder of audio files nobody has time to relisten to, and dashboards you can customize and white-label track themes and volume across your Thai recordings over time. One multilingual education platform put its assessment recordings, Thai included, through this kind of workflow across embedded recorders spanning multiple languages, without hiring reviewers for each one.

And because Thai recordings rarely live in isolation from the rest of your work, the same engine scores calls and coaches teams on the same criteria, connecting Thai transcription to call scoring, the wider قاعدة المعرفة, and the rest of your stack through the MCP server your team already runs on.