Back to the journalAudio Annotation: Transcription, Diarization and Events
Short answer: audio annotation means labeling sound recordings so speech and audio models can learn from them. The main tasks are transcription (writing down what was said, often with timestamps), speaker diarization (marking who spoke when), sound event labeling (marking non-speech sounds such as alarms or machinery), and classification of language, emotion or audio quality.
Main audio annotation tasks
| Task | What gets labeled | Used for |
|---|---|---|
| Transcription | Words spoken, with timestamps | Speech recognition, voice assistants, captioning |
| Speaker diarization | Who is speaking in each time segment | Meeting notes, call analytics |
| Sound event detection | Start and end of non-speech sounds | Safety monitoring, machine health, smart devices |
| Audio classification | One label per clip | Language ID, music genre, noise type |
| Phonetic transcription | Sounds in a phonetic alphabet | Pronunciation and accent models |
| Emotion or tone | Emotional state of the speaker | Customer experience analytics |
Transcription rules to agree on
- Verbatim or clean: keep "um", false starts and repeats, or remove them?
- Numbers and dates: "twenty-five" or "25"?
- Unclear speech: a standard tag such as [inaudible] with a timestamp.
- Overlapping speech: transcribe both speakers or mark overlap only?
- Non-speech sounds: tags such as [laughter], [music], [noise].
- Accents, code-switching and multiple languages.
Speaker diarization tips
- Decide the minimum segment length.
- Mark overlaps explicitly rather than splitting them arbitrarily.
- Keep speaker labels consistent through the whole recording (Speaker A stays Speaker A).
- Note when a speaker is unknown vs when they are named in the audio.
Sound event labeling tips
- Define each event class with an example recording.
- Set rules for how close two events can be before they merge.
- Mark start and end at agreed points (onset of the sound, not the visible waveform peak).
Quality checks for audio data
- Word error rate (WER) between an annotator and a reviewer on a sample of transcripts.
- Diarization agreement: compare speaker boundaries between two annotators.
- Temporal IoU for sound event start and end times.
- Gold clips with known transcripts in every batch.
- Native or fluent annotators for each language and accent.
Privacy
Voice recordings can identify people. Agree on consent, redaction of names and numbers, access controls and deletion before work starts.
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