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pith:OV4SSAHP

pith:2026:OV4SSAHPIPZCVVMNDPQR7ZCQB4
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Improving Automatic Speech Recognition for Speakers Treated for Oral Cancer using Data Augmentation and LLM Error Correction

Bence Mark Halpern, Hidde Folkertsma, Jiapan Guo, Max Witjes, Rob van Son, Sebastiaan de Visscher, Thomas Tienkamp

Combining data augmentation and LLM error correction cuts word error rates by 40-50% for oral cancer speech recognition.

arxiv:2605.15854 v1 · 2026-05-15 · eess.AS

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Claims

C1strongest claim

Overall, we achieve a 40% relative WER decrease for Whisper and a 50% relative WER decrease for MMS, indicating that a combination of data augmentation and LLM correction is a viable strategy for the recognition of OC speech.

C2weakest assumption

The synthetic data produced by the augmentation techniques sufficiently captures the acoustic variability of real oral cancer speech and the LLM corrections do not systematically alter medically relevant content.

C3one line summary

TTS data augmentation and LLM error correction together cut relative WER by 40-50% on ASR models for oral cancer speech.

References

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[1] The global incidence of lip, oral cavity, and pharyngeal cancers by subsite in 2012, 2012
[2] Cancer statistics for the year 2020: An overview, 2020 · doi:10.1002/ijc.33588
[3] Speech Deficits Associated with Oral and Oropharyngeal Carcinomas, 2019 · doi:10.1007/978-3-030-04702-3
[4] Speech Disorders Related to Head and Neck Cancer, 2021 · doi:10.1002/9781119606987.ch22
[5] Articulatory–kinematic changes in speech following surgical treatment for oral or oropharyngeal cancer: A systematic review, 2025 · doi:10.1111/1460-6984.13148

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First computed 2026-05-20T00:01:22.053845Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

75792900ef43f22ad58d1be11fe4500f304138f1b50c94237e0dcbc974d9c60d

Aliases

arxiv: 2605.15854 · arxiv_version: 2605.15854v1 · doi: 10.48550/arxiv.2605.15854 · pith_short_12: OV4SSAHPIPZC · pith_short_16: OV4SSAHPIPZCVVMN · pith_short_8: OV4SSAHP
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/OV4SSAHPIPZCVVMNDPQR7ZCQB4 \
  | 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: 75792900ef43f22ad58d1be11fe4500f304138f1b50c94237e0dcbc974d9c60d
Canonical record JSON
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