percept-speaker-id

Speaker identification and management for multi-person conversations.

What it does

Tracks who said what in conversations. Maps anonymous speaker labels (SPEAKER_0, SPEAKER_1) to real names, maintains speaker profiles, and gates voice command authorization.

When to use

• User asks "who said that?" or wants speaker-attributed transcripts
• User wants to configure which people can trigger voice commands
• Agent needs to know who is speaking in a multi-person conversation

Requirements

• percept-listen skill installed and running
• Omi pendant (provides is_user flag for primary speaker)

How it works

1. Omi sends transcript segments with speaker labels (SPEAKER_0, SPEAKER_1, etc.)
2. Percept resolves labels to names using the speakers registry
3. is_user flag from Omi identifies the pendant wearer as the primary speaker
4. Speaker profiles track first/last seen timestamps and authorization status

Speaker registry

Located at percept/data/speakers.json:

{
  "SPEAKER_00": {
    "name": "David",
    "is_owner": true,
    "approved": true
  },
  "SPEAKER_01": {
    "name": "Rob",
    "is_owner": false,
    "approved": true
  }
}

Manage via Percept dashboard (port 8960) → Settings → Speakers.

Authorization levels

• Owner (is_owner: true): Full command access, always authorized
• Approved (approved: true): Can trigger wake word commands
• Unknown: Logged only, commands not executed

Future: Voice embeddings

Planned: pyannote speaker diarization with 192-dim voice embeddings for automatic speaker recognition via cosine similarity. Currently speaker mapping is manual.

Links

• GitHub: https://github.com/GetPercept/percept