Gains:
- Ability to understand the concepts of automatic transcription and speaker separation and convert raw recording into time-coded text
- Ability to understand subtitle formats (SRT/VTT), reading speed and line rules, and produce and edit multilingual subtitle drafts
- Understanding that it is the editor's responsibility to verify terminology, proper names, punctuation errors and translation errors in the automatic transcript before publication.
The most tedious, time-consuming task of post-production was traditionally transcription: an editor would listen endlessly to hours of interview footage, typing out each sentence by hand. A one-hour recording would easily take four to five hours to transcribe. Artificial intelligence has radically changed this business: today an hour-long recording can be turned into time-coded text in minutes. This both speeds up the editing pipeline and enables accessibility (subtitles for hearing-impaired viewers and silent viewing). But automatic output is never suitable for publication in its raw form; In this unit we will see both the power and the pitfalls.
Let's start with the terms. Transcription is the putting of speech into writing. Automatic speech recognition (ASR) is technology that translates voice into text. Timecode is the sign that shows which second the text corresponds to in the video. Speaker diarization is distinguishing "who spoke" and dividing the text into speakers. Subtitle (subtitle/caption) is the time-coded text that appears on the screen. SRT and VTT are the most common subtitle file formats; They contain the order, start-end time, and text of each line. Reading speed (CPS: characters per second) determines how long the line must remain on the screen for the viewer to read the subtitle comfortably.
The power and limit of automatic transcription
AI transcription transcribes a clear, single-speaker voice recorded in proper Turkish with very high accuracy. But it makes mistakes in the following situations, and knowing these allows you to target verification: proper names (names of people, brands, places are often misspelled), technical terms and abbreviations, similar sounding words (“kar/kar”, “still/still”), overlapping speech (two people at the same time), background noise and music, dialect/accent differences and punctuation (question or sentence end). Additionally, the model may “hear” and transcribe a word that has never been spoken in a noisy moment—this is the hallucination of the transcription.
The following table summarizes the strong and weak conditions for automatic transcription:
condition
accuracy
Editor's focus
Single speaker, clear sound, quiet environment
very high
Proper names, punctuation
Multi-speaker panel
medium
Speaker separation, overlapping speech
Noisy/outdoor recording
low-medium
Made-up word, jump
Technical/jargon content
medium
Term and abbreviation writing
accented speech
Variable
Word errors, meaning
Subtitle format and readability rules
A good subtitle is not just "correct text"; must be readable. There are a few rules of thumb in international practice: a line of subtitles should generally be no more than two lines; each line should be kept to a reasonable length (approximately 37-42 characters); The subtitle must remain on the screen long enough to be read (usually 1-6 seconds); The reading speed should not be too high (most guides consider ~15-17 characters/second as the limit). AI tools automatically split the subtitle, but these splits sometimes cut the sentence in bad places (line ending with "and", break that divides the meaning). The editor's job is to make the text both accurate and fluent.
Your role: subtitle editor assistant. Below is an automatic transcript. Task:1) Divide the text into subtitle blocks according to SRT logic.2) Each block should be a maximum of 2 lines, each line should not exceed ~40 characters.3) Divide the sentence at meaningful places; Line ending with "and", "but", "that".4) Mark proper nouns and terms with the [CHECK] tag so I can verify them manually.5) Don't change the text, just split and mark.
The "[CONTROL] tag" idea in this prompt is valuable: Having the AI mark areas where it is unsure or risky (proper name, term) directs the editor's eye to the right spots and prevents blind trust.
Multilingual subtitles and translation
Opening a video to multiple languages increases the audience. The AI can take the transcript, translate it into the target language, and produce a subtitle file. This is a powerful capability, but it is the most vulnerable: machine translation can misrepresent idioms, cultural references, humor and puns, terms, and context. Translating the sentence "It's raining cats and dogs" word for word is a disaster. There is also a space constraint in subtitle translation: the sentence in the target language may be longer and exceed the reading speed. That's why it's essential for multilingual subtitling to have a person who knows the target language verify the translation — especially for sensitive content such as branding, legal or health, incorrect translation can have serious consequences.
Translate the following Turkish subtitle blocks into English. Rules: - Transfer the idiom and joke, not verbatim, but preserving their MEANING. - Leave the brand name and product name as they are, do not translate. - Let each block maintain its reading speed; Shorten if necessary, but do not distort the meaning. - Specify the cultural reference or term you are not sure about with [TRANSLATOR'S NOTE].
three mini cases
Case 1 — Accelerating line. A documentary crew spent three weeks hand transcribing a 12-hour archive of interviews. With automatic transcription, the raw text was output in a day; By searching via text (word by word), the team found the moments they wanted in seconds. Then they meticulously edited and subtitled only the 40-minute episode they would use. Three weeks turned into four days; verification effort focused on the section used.
Case 2 — Made-up word. A news channel broadcast the raucous street interview with automatic subtitles, without checking. The model "heard" an unspoken word in the background hum, and the caption imputed an unspoken word to the interviewee. There was a reaction on social media and the correction was published. Lesson: in noisy recordings, automatic subtitles must be compared with the original audio.
Case 3 — Translation disaster. A brand published the English subtitles of its promotional video with machine translation and without control. When a Turkish idiom ("keep an eye out") was translated word for word, it seemed meaningless and even funny to the English audience, and the seriousness of the brand was damaged. The right way: to have the translation verified by someone who knows the target language.
Weak prompt / Strong prompt
Weak prompt:
Subtitle this video and translate it into English.
There is no formatting, no readability rules, no validation marks. The output will be crude and risky.
Powerful prompt:
Your role: bilingual subtitle editor.Input: Turkish timecoded transcript.Task:1) Edit Turkish subtitle by 2 lines / ~40 characters / good division rules.2) Mark proper names and terms with [CONTROL].3) Produce English translation separately; Transfer the phrase literally, preserve the brand name.4) Mark blocks that exceed the reading speed with [LONG].5) Do not make up any words; Write the indefinite sound [UNINDICATED].
Common mistakes
- Publishing automatic transcripts without control. Errors in proper names, terms, punctuation and made-up words remain.
- Bad line splitting. Lines ending in "and/but/ki" make reading difficult.
- Exceeding reading speed. Long subtitles that are too short on the screen cannot be read.
- Not verifying machine translation. If an idiom, joke or term goes wrong, the brand suffers.
- Bypassing speaker distinction. If there is confusion about "who said what" in the panel, the subtitle will be misleading.
Tip: While editing the transcript, watch the video at 1.25-1.5 speed and check that it is synchronized with the subtitles. This way you quickly catch both timecode drift and made-up/missed words.
Caution: In fields such as healthcare, law, finance, a single incorrectly transcribed word (e.g. a dose, an ingredient number) can have serious consequences. In these contents, each number and term must be separately verified.
In summary
Automated transcription and subtitling are post-production's biggest time saver and enable accessibility. AI reduces hours of transcription into minutes and enables searching via text. But the output is not suitable for publication in its raw form: proper names, terms, punctuation, made-up words and bad line breaks must be corrected. Readability rules (line length, duration, reading speed) make the text fluent for the viewer. In multilingual subtitles, the machine translation must be verified by a person who knows the target language. AI pours and suggests; Accuracy, readability and meaning are the responsibility of the editor.
Application task
Take a short conversation recording (maybe your own voice, 2-3 minutes) and have it automatically transcribed. Compare the output with the original audio and flag proper nouns, terms, and punctuation errors; Find at least five errors. Then, have the text divided into subtitle blocks according to the rules above, translated into one language, and correct at least two risky points (idioms/terms) in the translation. Report the process and any errors you find.
checklist
- [ ] Have I compared the transcript to the original audio and corrected any proper noun/term/punctuation errors?
- [ ] Have I checked for made-up or omitted words?
- [ ] Have I edited the subtitle according to line length, duration and reading speed rules?
- [ ] For multilingual subtitles, have I carefully verified the translation with someone who knows the target language?
- [ ] Have I separately verified each number and term in sensitive content?