Overview
Speaker embedding models encode a variable-length speech segment into a fixed-size vector that captures the speaker's vocal identity. These embeddings are the backbone of speaker verification (is this the claimed speaker?) and identification (which speaker is this?).
Agent Scenarios
- Voice authentication agent: verify a user's identity by comparing a live utterance embedding against a stored voiceprint before granting access
- Personalization agent: adapt a voice assistant's responses or TTS voice profile based on recognized speaker identity
- Fraud detection agent: flag calls where the speaker embedding diverges from the account holder's enrolled voiceprint
- Speaker-aware RAG agent: retrieve documents personalized to a known speaker's history or preferences, identified from an audio query
ModelKit Integration
Models must pass the full wmk pipeline on all EPs:
wmk config → wmk build (ONNX export) → wmk perf → wmk eval
Acceptance Criteria
Overview
Speaker embedding models encode a variable-length speech segment into a fixed-size vector that captures the speaker's vocal identity. These embeddings are the backbone of speaker verification (is this the claimed speaker?) and identification (which speaker is this?).
Agent Scenarios
ModelKit Integration
Models must pass the full wmk pipeline on all EPs:
Acceptance Criteria