Training-Free Affinity Fusion of Neural and Embedding-Based Speaker Diarization
Speaker diarization systems based on speaker embeddings and neural diarization exploit complementary forms of speaker information, but their intermediate representations are not directly compatible. We introduce Training-Free Affinity Fusion (TFAF), which integrates the speaker structure inferred by a neural diarizer into an embedding-based diarization system. The neural speaker partition is used to condition local speaker representations, from which we construct a continuous affinity matrix and combine it with the embedding-based acoustic affinity before a single global clustering step. The method requires no additional training, shared embedding space, speaker-label alignment, or hard t...
| Authors | Yehoshua Dissen, Joseph Keshet, Eduard Golshtein |
|---|---|
| Primary category | cs.SD |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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