pith:7ECUX2WT
Explainable AI in Speaker Recognition -- Making Latent Representations Understandable
Speaker recognition neural networks organize their latent representations into hierarchical clusters that align with semantic attributes like gender and nationality.
arxiv:2604.23354 v2 · 2026-04-25 · eess.AS · cs.AI · eess.SP
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\pithnumber{7ECUX2WT3FVS2IHK4OZBKP6AL6}
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Claims
This work applies SLINK and HDBSCAN to demonstrate the existence of hierarchical clustering phenomena within the network representation space, and designs HCCM to perform one-to-one matching between predefined semantic classes and hierarchical representation clusters, with Liebig's score to quantify performance.
That the hierarchical clusters produced by SLINK or HDBSCAN correspond to meaningful semantic classes or their conjunctions in a non-arbitrary way that HCCM can reliably detect and that Liebig's score meaningfully diagnoses limiting factors.
Speaker recognition networks form hierarchical clusters in latent space that can be matched to semantic classes using new HCCM algorithm and quantified by Liebig's score.
Receipt and verification
| First computed | 2026-05-29T01:05:10.514741Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f9054bead3d96b2d20eae3b2153fc05f965498ef02a3c8ce2306eff1901e3ba4
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/7ECUX2WT3FVS2IHK4OZBKP6AL6 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f9054bead3d96b2d20eae3b2153fc05f965498ef02a3c8ce2306eff1901e3ba4
Canonical record JSON
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