Voice embeddings

A voice embedding is a vector - usually between 128 and 256 floating-point numbers - that represents how a voice sounds. Two clips of the same person produce vectors that are close together in cosine distance; two clips of different people produce vectors that are far apart. The embedding is content-invariant: it does not encode what was said, only how it sounded.

Mac Note Taker uses CAM++, a 192-dimensional embedding model, to fingerprint every speaker turn after diarization. At ~770 bytes per turn, even a heavy meeting habit produces under 100MB of embedding data across a full year. These fingerprints are matched against a local profile library using cosine similarity (default threshold 0.65) to reproduce names across meetings without any cloud lookup.