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pith:NQYXZCKC

pith:2026:NQYXZCKCANGSIAIJLWC56WWKO7

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TW-Sound580K: A Regional Audio-Text Dataset with Verification-Guided Curation for Localized Audio-Language Modeling

Hao-Hui Xie, Ho-Lam Chung, Hung-yi Lee, Ke-Han Lu, Wenze Ren, Xie Chen, Yi-Cheng Lin

A verification-curated Taiwanese audio-text dataset and dynamic arbitration strategy lifts audio-language model accuracy on localized speech from 42.6 to 49.1 percent.

arxiv:2603.05094 v3 · 2026-03-05 · cs.SD

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Claims

C1strongest claim

On the TAU Benchmark, Tai-LALM reaches 49.1% accuracy, marking a 6.5% absolute improvement over the zero-shot baseline (42.6% with ASR text conditioning). This confirms that integrating regional corpora with rigorous curation and dynamic arbitration significantly enhances LALM performance on localized speech.

C2weakest assumption

That the Verify-Generate-Critique protocol combined with Dual-ASR validation produces genuinely higher-fidelity instruction pairs that causally drive the observed benchmark gain rather than other unstated differences in training or evaluation.

C3one line summary

TW-Sound580K dataset plus Tai-LALM model with dynamic Dual-ASR arbitration lifts localized Taiwanese audio-language accuracy to 49.1% on the TAU benchmark.

References

44 extracted · 44 resolved · 6 Pith anchors

[1] TW-Sound580K: A Regional Audio-Text Dataset with Verification-Guided Curation for Localized Audio-Language Modeling 2026 · arXiv:2603.05094

[2] Background Foundational corpora like AudioSet [11] and LibriSpeech

[3] acoustic long-tail

[4] Methodology To bridge the localization gap, our data-centric pipeline is struc- tured into four key stages: (I) Dataset Construction, (II) Train- ing Data Generation, (III) the multimodal Training Pro

[5] To preserve speech-free soundmarks, clips where both ASRs yield empty outputs bypass the text check

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TW-Sound580K: A Regional Audio-Text Dataset with Verification-Guided Curation for Localized Audio-Language Modeling

Receipt and verification

First computed	2026-05-18T03:09:23.120726Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6c317c8942034d2401095d85df5aca77ef6b71410b43bf4606c95b3955ab0bae

Aliases

arxiv: 2603.05094 · arxiv_version: 2603.05094v3 · doi: 10.48550/arxiv.2603.05094 · pith_short_12: NQYXZCKCANGS · pith_short_16: NQYXZCKCANGSIAIJ · pith_short_8: NQYXZCKC

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/NQYXZCKCANGSIAIJLWC56WWKO7 \
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  | 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: 6c317c8942034d2401095d85df5aca77ef6b71410b43bf4606c95b3955ab0bae

Canonical record JSON

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