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pith:SOH5VECQ

pith:2026:SOH5VECQLZ76DNVTH6JJPW3OZ6

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Mind the Gap: Impact of Synthetic Conversational Data on Multi-Talker ASR and Speaker Diarization

Alexander Polok, Ivan Medennikov, Jan \v{C}ernock\'y, Luk\'a\v{s} Burget, Samuele Cornell, Shinji Watanabe

Synthetic conversational data approaches real-data baselines and mixing both yields substantial gains for multi-talker ASR and speaker diarization.

arxiv:2605.15442 v1 · 2026-05-14 · eess.AS

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Claims

C1strongest claim

synthetic-only training approaches real-data baselines, and combining simulated data with real recordings yields substantial gains over real-only training across both tasks.

C2weakest assumption

The specific simulation choices and acoustic augmentations in FastMSS produce mixtures whose statistical properties are close enough to real conversational recordings that performance trends observed on synthetic data will transfer to real-world use.

C3one line summary

Task-dependent simulation strategies for synthetic conversational data allow synthetic-only training to approach real-data baselines for multi-talker ASR and diarization, with mixing yielding further gains.

References

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[1] Introduction Multi-talker conversational speech processing is undergoing a rapid transformation, driven largely by the shift from highly specialized pipelines to less data-hungry methods built on pret

[2] Mind the Gap: Impact of Synthetic Conversational Data on Multi-Talker ASR and Speaker Diarization 2026 · arXiv:2605.15442

[3] Multi-Speaker Conversation Simulation To enable controlled and fast experimentation along the axes described above, we developed FastMSS, an open-source multi- speaker conversation simulator focused o

[4] Experimental Setup 4.1. Datasets As source domains for synthetic generation, we use: Lib- riSpeech [49] (read speech, 960h), V oxPopuli [50] (semi- spontaneous parliamentary speech, 543h), otoSpeech [

[5] All datasets were re-aligned using the Montreal Forced Aligner [52] to ensure consistent word-level timestamps

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Mind the Gap: Impact of Synthetic Conversational Data on Multi-Talker ASR and Speaker Diarization

Receipt and verification

First computed	2026-05-20T00:00:58.798834Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

938fda90505e7fe1b6b33f9297db6ecfb4f833dcfcd2f4ee65238276b5fdcec2

Aliases

arxiv: 2605.15442 · arxiv_version: 2605.15442v1 · doi: 10.48550/arxiv.2605.15442 · pith_short_12: SOH5VECQLZ76 · pith_short_16: SOH5VECQLZ76DNVT · pith_short_8: SOH5VECQ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SOH5VECQLZ76DNVTH6JJPW3OZ6 \
  | 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: 938fda90505e7fe1b6b33f9297db6ecfb4f833dcfcd2f4ee65238276b5fdcec2

Canonical record JSON

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    "primary_cat": "eess.AS",
    "submitted_at": "2026-05-14T21:53:10Z",
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