Pith Number

pith:P3ZKMQR3

pith:2026:P3ZKMQR36M6DEYDELXYZGD5OFW

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Does language matter for spoken word classification? A multilingual generative meta-learning approach

Batsirayi Mupamhi Ziki, Louise Beyers, Ruan van der Merwe

Multilingual spoken word classification shows only small gains over monolingual models, with training data volume outweighing language count.

arxiv:2605.13084 v2 · 2026-05-13 · cs.CL · cs.AI

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We find that although the multilingual model performs best, the differences between model performance is unexpectedly low. We also find that the hours of unique data seen during training seems to be a stronger performance indicator than the number of languages included in the training data.

C2weakest assumption

That the Generative Meta-Continual Learning algorithm transfers effectively to multilingual spoken word classification without requiring language-specific modifications or additional regularization.

C3one line summary

Multilingual generative meta-learning for spoken word classification shows small gains over monolingual models, with unique data volume mattering more than the number of languages.

References

41 extracted · 41 resolved · 0 Pith anchors

[1] Proceedings of the 35th International Conference on Neural Information Processing Systems , articleno = 2021

[2] Three types of incremental learning , volume =

[3] Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) , year=

[4] HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units , year=

[5] Proceedings of the 34th International Conference on Neural Information Processing Systems , articleno = 2020

Receipt and verification

First computed	2026-05-18T03:08:58.584523Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7ef2a6423bf33c3260645df1930fae2da1fa51c3e868975ed9e188745bfac7fc

Aliases

arxiv: 2605.13084 · arxiv_version: 2605.13084v2 · doi: 10.48550/arxiv.2605.13084 · pith_short_12: P3ZKMQR36M6D · pith_short_16: P3ZKMQR36M6DEYDE · pith_short_8: P3ZKMQR3

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/P3ZKMQR36M6DEYDELXYZGD5OFW \
  | 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: 7ef2a6423bf33c3260645df1930fae2da1fa51c3e868975ed9e188745bfac7fc

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "ae20b59064df3d7aaf7e96c58e01c79853d6e44341b05df94792f8eb3e9c6693",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-05-13T06:54:51Z",
    "title_canon_sha256": "819ec47c8b639955dab995be59e4b81aa6f67ca948b7ebe86dd4f99e050cbe68"
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  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 2
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}