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Paper Citation Record · LEDGER

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition

As of 9 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2509.19817.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.19817 v3

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:22:18.839404Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:22:16.346725Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved34
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 01416b95-20cd-45e2-b428-7ea445bf9c33 · outbound

This paper cites MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition

Reference 1

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source=pdf_text observed=2026-08-04T15:22:16.346725Z digest=sha256:b456315292044390520dc3bac1e61b911625be0255f9fc946ce54bbfabd1cc3c

Observation 649911c2-2ac0-4b9f-bea2-32b0920535c6 · outbound

This paper cites an unresolved cited work.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Unresolved cited work

Reference 2

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source=pdf_text observed=2026-08-04T15:22:16.374793Z digest=sha256:d8f7402cfe6088b9f4a1e3ac961220c7f8af77d1c9db7c5ef3abd7e9c95dc3b5

Observation c1b527c2-278d-41df-b688-e1cf3bb93fa7 · outbound

This paper cites an unresolved cited work.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-04T15:22:16.395436Z digest=sha256:80f23354908d8ab4b4b2f1f20fb601cfc27dba86f6f89eed5209542f56eb4249

Observation 8fa8267e-00f7-46d3-91eb-1db53ad5b906 · outbound

This paper cites an unresolved cited work.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-04T15:22:16.413873Z digest=sha256:8136b1ae34126c0f6b9421fb1273a2635e3a480ed9099223d9946b8ca51869c6

Observation 3beceeed-091e-47f7-8c22-36fe5b4c48c9 · outbound

This paper cites Data Acquisition We collected speech from a full-duplex healthcare assistant during internal testing (beta version).

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Data Acquisition We collected speech from a full-duplex healthcare assistant during internal testing (beta version)

Reference 5

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source=pdf_text observed=2026-08-04T15:22:16.421986Z digest=sha256:c0da5b7229953f3eafff72f76f64e6f11a99f083d654f64809fa48a5d6ed5a1c

Observation 39c74ce4-1db1-445c-9c16-f10957b32bc8 · outbound

This paper cites Experimental Setups We fine-tuned Whisper-small [14] end-to-end on the Chinese training split for automatic speech recognition in healthcare dialogue.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Experimental Setups We fine-tuned Whisper-small [14] end-to-end on the Chinese training split for automatic speech recognition in healthcare dialogue

Reference 6

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source=pdf_text observed=2026-08-04T15:22:16.440505Z digest=sha256:bf97c33917b6dac7bf9b5eb142cc2bcb52393c04a59ac6065e61d09f13667230

Observation 2139dc29-1c71-4c84-b24b-95e52315d078 · outbound

This paper cites Benchmark Description MMedFD is a benchmark for Chinese healthcare spoken di- alogue constructed from live user–agent interactions under full-duplex conditions.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Benchmark Description MMedFD is a benchmark for Chinese healthcare spoken di- alogue constructed from live user–agent interactions under full-duplex conditions

Reference 7

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source=pdf_text observed=2026-08-04T15:22:16.459548Z digest=sha256:63a7bb6a647d3d4dd3912a4c07ffda7b0aff4d331c35d4f7a8c8c2225d8558f1

Observation e8541f26-1d29-4030-a3b6-61de438dca0c · outbound

This paper cites LLM-judged results for healthcare queries using PairEval and G-Eval with a consistent GPT-5 judge.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition LLM-judged results for healthcare queries using PairEval and G-Eval with a consistent GPT-5 judge

Reference 8

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source=pdf_text observed=2026-08-04T15:22:16.479683Z digest=sha256:66910f6715f4c1f9fe4e24e3da1056a5571f17185d07799a5757c9d3361eeac7

Observation e48a6ac4-9485-4a54-a45d-823edc0ccaf4 · outbound

This paper cites P0051278, Jung Sun Yoo) and by the Research Grants Council of the Hong Kong Special Administrative Region, China (General Re- search Fund, Project No.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition P0051278, Jung Sun Yoo) and by the Research Grants Council of the Hong Kong Special Administrative Region, China (General Re- search Fund, Project No

Reference 9

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source=pdf_text observed=2026-08-04T15:22:16.498586Z digest=sha256:6fbafe8ed271702595efb1e4693afe6a5f509dfa3dffcfdb0ab734d289243b25

Observation 1f285f29-c215-4f79-a973-9a3dfb789d4b · outbound

This paper cites The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models

Reference 10

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source=pdf_text observed=2026-08-04T15:22:16.516308Z digest=sha256:c73b865866aa23cf0cca7040f295ddf2d790e52789a9a38a639501f44b7033b5

Observation 5e74c4ea-0f3a-4e9d-ba0f-e1a8147d0af9 · outbound

This paper cites Medical dialogue system: A survey of cat- egories, methods, evaluation and challenges,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Medical dialogue system: A survey of cat- egories, methods, evaluation and challenges,

Reference 11

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source=pdf_text observed=2026-08-04T15:22:16.533560Z digest=sha256:22a6d51e9c0e78630aa9eb740911ecef3f7bd1176389b0470ba812f5a44ae9d3

Observation 0b5aef1e-162a-4672-9479-ac6746e7ece1 · outbound

This paper cites Multimed: Multilingual medi- cal speech recognition via attention encoder decoder,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Multimed: Multilingual medi- cal speech recognition via attention encoder decoder,

Reference 12

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source=pdf_text observed=2026-08-04T15:22:16.542090Z digest=sha256:b57f0d9130a0e778e5718579328479c71c782b085d693a0aa192f5ca1d8e4cd7

Observation e4f1f9e1-5cb7-43aa-8714-bfe04932987f · outbound

This paper cites The AI doctor is in: A survey of task-oriented dialogue systems for healthcare appli- cations,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition The AI doctor is in: A survey of task-oriented dialogue systems for healthcare appli- cations,

Reference 13

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source=pdf_text observed=2026-08-04T15:22:16.554319Z digest=sha256:aceb418663ae8f2806235ca471904d46911b49f927fe134046b55296d8766bf2

Observation 5f00b907-f0b3-47d6-bca7-738c349cf809 · outbound

This paper cites A full-duplex speech dialogue scheme based on large language model,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition A full-duplex speech dialogue scheme based on large language model,

Reference 14

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source=pdf_text observed=2026-08-04T15:22:16.618816Z digest=sha256:85b1c79d1546c10d0df8323cfc8329f2e8fba193158a7c005ffef4b88acf392b

Observation 1d54ba5c-a68b-46f1-ada7-435d97fdf422 · outbound

This paper cites Primock57: A dataset of primary care mock consultations,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Primock57: A dataset of primary care mock consultations,

Reference 15

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source=pdf_text observed=2026-08-04T15:22:16.779249Z digest=sha256:64c589011fa926060bdbcc244e225e1e2234b192e7bd33fc570406532a9b44c3

Observation 3f14d887-cffa-4a98-bf23-b5228285741a · outbound

This paper cites Mtalk-bench: Evaluating speech-to- speech models in multi-turn dialogues via arena-style and rubrics protocols,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Mtalk-bench: Evaluating speech-to- speech models in multi-turn dialogues via arena-style and rubrics protocols,

Reference 16

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source=pdf_text observed=2026-08-04T15:22:16.839554Z digest=sha256:1416a96ed6379ffe5a75e8ade10ca7219cf4c9ca3c77bacffa42c82a145fd100

Observation ab8d67bf-2161-42f8-9cde-8ff01c3e3e80 · outbound

This paper cites an unresolved cited work.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-04T15:22:16.923506Z digest=sha256:3f7747ce8f2f028445689600ff35f2822733e706359a932ccb581f12ca89fcd9

Observation 894c153e-c88d-43be-87d7-9d6aa087ae33 · outbound

This paper cites KWS15 keyword search evaluation plan,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition KWS15 keyword search evaluation plan,

Reference 18

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source=pdf_text observed=2026-08-04T15:22:17.094461Z digest=sha256:06a1415ef578939d14a7899a657565528c796de6acc278e75dd94a5d93845a45

Observation 34bedba4-41ea-4e66-8ea1-120ad1f891c4 · outbound

This paper cites The bigscience roots corpus: A 1.6tb composite multilingual dataset,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition The bigscience roots corpus: A 1.6tb composite multilingual dataset,

Reference 19

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source=pdf_text observed=2026-08-04T15:22:17.169949Z digest=sha256:941e0b3183303955ff08e57ffb5f0f9f427f4ea647c908e796f614ae5cc503fd

Observation 26840883-4ca7-46d8-b1ff-c12214ed482a · outbound

This paper cites Silero vad: Pre-trained enterprise- grade voice activity detector,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Silero vad: Pre-trained enterprise- grade voice activity detector,

Reference 20

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source=pdf_text observed=2026-08-04T15:22:17.285592Z digest=sha256:47273c6e0184943b1834f2b914bc267d70c896480ada774989363bb2183fb278

Observation 0a4a3281-f989-4785-8070-a5c5ff17c8f6 · outbound

This paper cites pyannote.audio: neural building blocks for speaker diarization.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition pyannote.audio: neural building blocks for speaker diarization

Reference 21

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source=pdf_text observed=2026-08-04T15:22:17.337394Z digest=sha256:dc331e394c73a5e27511d15484f5897696daee8604e8b2d2035d138da4b8a504

Observation 33cd10b0-5940-4a70-b83d-30007e92a6e1 · outbound

This paper cites Openasr21 challenge evaluation plan,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Openasr21 challenge evaluation plan,

Reference 22

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source=pdf_text observed=2026-08-04T15:22:17.461986Z digest=sha256:fe314b881e52a05e1c7cdffb7d677879868b9022efa0e19ffa283985df3d99c2

Observation bef2036b-edbb-46f9-9934-367d5a43e1f6 · outbound

This paper cites Robust speech recognition via large-scale weak supervision,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Robust speech recognition via large-scale weak supervision,

Reference 23

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source=pdf_text observed=2026-08-04T15:22:17.565485Z digest=sha256:8259d8b8eab3c426fb44bf8cd0cd2e33543e89dd7ff2af60eb40a7786c36dd0b

Observation 6b0898ae-af83-4fcb-900a-f545ad8db60c · outbound

This paper cites Paireval: Open-domain dialogue evaluation with pairwise comparison,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Paireval: Open-domain dialogue evaluation with pairwise comparison,

Reference 24

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source=pdf_text observed=2026-08-04T15:22:17.648500Z digest=sha256:f1ece73fe1e2525c729bce11e561fcfd4fc1f5d2ac872ba762229e4ffe6942a6

Observation 6acfa2f3-f6d1-4e1f-8ed6-b6f0cd6b5916 · outbound

This paper cites G-eval: NLG evaluation using GPT- 4 with better human alignment,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition G-eval: NLG evaluation using GPT- 4 with better human alignment,

Reference 25

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source=pdf_text observed=2026-08-04T15:22:17.821686Z digest=sha256:282c1be9e0caab635468261e3b1545ec9eacf2bb51d4c794b5c3a35bd40ab6bc

Observation 73297133-b368-4a9d-a926-db0b443bb430 · outbound

This paper cites Vietmed: A dataset and benchmark for automatic speech recognition of vietnamese in the medical domain,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Vietmed: A dataset and benchmark for automatic speech recognition of vietnamese in the medical domain,

Reference 26

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source=pdf_text observed=2026-08-04T15:22:17.975254Z digest=sha256:db1e53e6ca639feae6e8b98361d321af1bf4498530a75f699bb80aaa3caacf69

Observation 7d2f68ec-bbee-4fc7-9ba9-4f76d115b94b · outbound

This paper cites A dataset of simulated patient- physician medical interviews with a focus on respiratory cases,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition A dataset of simulated patient- physician medical interviews with a focus on respiratory cases,

Reference 27

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source=pdf_text observed=2026-08-04T15:22:18.044820Z digest=sha256:89b836f49d4f7531b553f5d9fae6cd2ee5ff29919adfff4fe303b61bc1a22122

Observation f35f820f-5152-4e34-a199-613f2e0ef8ce · outbound

This paper cites mymedicon: End-to-end burmese automatic speech recognition for medical conversa- tions,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition mymedicon: End-to-end burmese automatic speech recognition for medical conversa- tions,

Reference 28

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source=pdf_text observed=2026-08-04T15:22:18.164460Z digest=sha256:64cb77b8d0e9180eea37d7e58daf8e216fc21729e2fdc1d31bf546aa01bbdf5f

Observation aeddf85a-e2c2-44ba-b170-ec2c63c8b7d7 · outbound

This paper cites Afrispeech-200: Pan-african ac- cented speech dataset for clinical and general domain asr,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Afrispeech-200: Pan-african ac- cented speech dataset for clinical and general domain asr,

Reference 29

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source=pdf_text observed=2026-08-04T15:22:18.271000Z digest=sha256:edddd3d47abc17cc33747302dbe89685e816cfda0579fea36e0d61c6b363c7d1

Observation 2019cf75-acdf-4c93-99a1-332bdb30537d · outbound

This paper cites Spokenwoz: A large-scale speech-text benchmark for spoken task-oriented dia- logue agents,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Spokenwoz: A large-scale speech-text benchmark for spoken task-oriented dia- logue agents,

Reference 30

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source=pdf_text observed=2026-08-04T15:22:18.335130Z digest=sha256:5f1eb4de4b617c8f0778a6414591cca50de9f4172c670fe41778df5310b74f78

Observation fec3f24b-48cb-4065-a351-4234d4267559 · outbound

This paper cites V oxdialogue: Can spoken dialogue systems understand information beyond words?,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition V oxdialogue: Can spoken dialogue systems understand information beyond words?,

Reference 31

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source=pdf_text observed=2026-08-04T15:22:18.435260Z digest=sha256:d30970fd98f282f7d8b425c8d81546fb890039836d469e5a1c6be69662ef1a5a

Observation 1d1d5f7f-eb21-4e7e-a131-82ebec3e0d79 · outbound

This paper cites Gpt-5 system card,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Gpt-5 system card,

Reference 32

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source=pdf_text observed=2026-08-04T15:22:18.536910Z digest=sha256:6527896a60b46e391139559c1f79888cd03a63be4a15024d32c1afe37a198f2a

Observation 79371a43-b042-462f-ac39-a0d86e6f3693 · outbound

This paper cites Claude opus 4.1 system card addendum,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Claude opus 4.1 system card addendum,

Reference 33

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source=pdf_text observed=2026-08-04T15:22:18.612090Z digest=sha256:165f9cbcd1ef340276622fb3ad3d18a92158738dd11082d1d48fee794ede93e7

Observation 8efe0fc2-edff-4212-bb67-07352adbcf07 · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 34

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source=pdf_text observed=2026-08-04T15:22:18.751402Z digest=sha256:51093a9c783154d70617cc7390dd4301c9610698910a8e7dbaa6f1b395781c9c

Observation 7b4e32d7-8939-4d87-8f45-d77b807548f1 · outbound

This paper cites Qwen3 Technical Report.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Qwen3 Technical Report

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T15:22:18.839404Z digest=sha256:0a6defc46d8e671869cdc7cc85a4d7b57ed1dea595430b0f95830c04a2f5b69a

Pith citing papers

Observation 01416b95-20cd-45e2-b428-7ea445bf9c33 · inbound

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition cites this paper.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition

Reference 1

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no resolver link, observed 2026-08-04T15:22:16.346725Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-04T15:22:16.346725Z digest=sha256:b456315292044390520dc3bac1e61b911625be0255f9fc946ce54bbfabd1cc3c