Speaker Context Layer

AI knows who logged in. It has no idea who is in the room.

A local MCP server that tells any AI assistant who is speaking. Voiceprints never leave your machine — no account, no cloud.

[Sammy]: We push the follow-up to Thursday.
[Amara]: I'll own the documentation pack.
[Wei]: Can we revisit the budget line first?
[UNKNOWN SPEAKER]: I don't agree with that.

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Try it in two minutes

pip install git+https://github.com/sammyghe/speaker-context-layer.git
scl-demo

It asks for a name, records 8 seconds, repeats for each person — then guesses who is speaking. Nothing is kept: scl-demo --reset deletes it.

Use it in an assistant

Claude Code

claude mcp add speaker-context-layer -- speaker-context-layer

Claude Desktop — add to claude_desktop_config.json:

{ "mcpServers": { "speaker-context-layer": { "command": "speaker-context-layer" } } }

Then say:

Use record_and_enroll to save my voice as Sammy in English — I consent, verbally, right now.

Setup for Gemini CLI and troubleshooting: docs/mcp-clients.md

Where it works, honestly

Claude Code, Claude Desktop, Gemini CLI	Yes — local, today
Claude mobile / voice	Not yet — needs a remote server
ChatGPT text	Poorly — connectors are mostly limited to search/fetch
ChatGPT voice mode	No — MCP tools are switched off during voice conversations

Voice mode is the least available place, which is the opposite of what you'd guess. An MCP tool call carries text — by the time the model calls a tool, the audio is already transcribed and gone, so no tool can reach the waveform it would need. Speaker identity has to be computed alongside a voice conversation by something holding the microphone, which is exactly what scl-room does.

Full explanation, and what a Connector Directory listing would cost: docs/where-it-runs.md

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What it does that others don't

One person, several voiceprints — one per language. A voice embedding shifts when you switch language, so a code-switching speaker reads as two different people. Enroll each person once per language they use:

enroll_speaker(name="Sammy", language="en", ...)
enroll_speaker(name="Sammy", language="lg", ...)

It refuses to guess. Three answers, not two: a name, NEW_SPEAKER, or AMBIGUOUS when two people score too closely. Misattributing a decision is worse than admitting you don't know.

The threshold is yours, not inherited. Everyone else ships one number tuned on English-heavy data. On other accents that number is wrong — and it fails silently, returning a confident wrong name. So it ships unset, every answer is marked calibrated: false, and you fix it with your own voices:

scl-calibrate ./clips --population "Kampala team, EN/LG" --apply

It reports the gap between your worst genuine match and your best impostor — and refuses to invent a threshold when the two overlap.

The reasoning, the evidence, and what would prove it wrong: THESIS.md

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Not authentication

Use it to label a transcript, follow a conversation, or caption a meeting. Never to unlock anything, approve a payment, gate access, or stand in for a signature. It cannot detect a recording or a cloned voice. A 0.96 score is not a signature.

Tools

Tool
record_and_enroll	Record a consenting person, store the print, delete the clip
record_and_identify	Record, identify, delete the clip
enroll_speaker	Store a voiceprint from a file
identify_speaker	Attribute a clip, or return NEW_SPEAKER / AMBIGUOUS
list_known_speakers	Roster, languages, consent records
forget_speaker	Erase one language or the whole person
list_microphones	Available inputs
calibration_status	Whether the threshold has been tested on your voices

Writing tools refuse without consent_confirmed=true and a consent_method describing how the person agreed.

Your data

~/.speaker-context-layer/registry.json

Voiceprints, consent records, your threshold. Treat it as biometric data — it is gitignored, and CI fails the build if audio or a registry is ever committed. See SECURITY.md.

Honest status

v0.1.0. The logic is tested (22 tests, ~0.5s). Accuracy on real voices is not established — no calibration run has happened yet. That is the next real step, and the software tells you so on every answer instead of hiding it.

Composes with

pyannote.audio (MIT) for diarization on long recordings — this project does not attempt it. Picovoice Eagle drives the experimental live room (scl-room).

Roadmap

• [ ] Calibration on real Ugandan English, Luganda, and Swahili-English code-switching
• [ ] Published calibration profiles per population, so others start from a real number
• [ ] Evaluate Intron Sahara as an African-language embedding backend
• [ ] Consent ceremony on first contact — chime, ask, wait, then enroll

Credits

Built by Sammy Gedamu with Claude Code as engineering coworker — architecture, research, and implementation paired throughout.

MIT — see LICENSE.