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Paper Citation Record · LEDGER

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems

As of 7 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2607.14846.

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

pith.paper-citation-record.v1
2607.14846 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T00:57:37.822940Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:41:57.270130Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T00:42:04.169139Z

Reference resolution

33 of 33 outbound references displayed

  • verified exact5
  • verified fuzzy0
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6901edd0-7c93-41d8-93c2-2b599aaec0d4 · outbound

This paper cites Cambridge University Press, 2018.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Cambridge University Press, 2018

Reference 1

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doi, observed 2026-08-02T00:58:19.916109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-02T00:57:37.670553Z digest=sha256:b26fc92a27c2f7b9a5bed5ee37cb39b6a98105b0dfe1099dba8e242f3573ed25

Observation b7a3967b-7839-4323-8715-007f10a13ba0 · outbound

This paper cites John Benjamins, 2010.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems John Benjamins, 2010

Reference 2

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doi, observed 2026-08-02T00:58:19.796619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-02T00:57:37.675403Z digest=sha256:31843fdb934405bc525b4488ba5df21ff5d3dfd3c8919c24be181344739c9e25

Observation 3639aa17-d0e5-4dc0-b1e7-7b5802743b3e · outbound

This paper cites Oxford University Press, 2021.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Oxford University Press, 2021

Reference 3

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source=pdf_text observed=2026-08-02T00:57:37.679716Z digest=sha256:1092f699387382d5acdf8661fede619689b5eb9e43707b97fd1c2355637e79bd

Observation 5652f4c9-85d8-482f-b395-0d8d69c67f0d · outbound

This paper cites Cambridge University Press, 1980.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Cambridge University Press, 1980

Reference 4

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source=pdf_text observed=2026-08-02T00:57:37.683927Z digest=sha256:0c16a1316c40adb00eae1e2fb2384912894055add9503983803bee1331fb02ca

Observation fb5e0384-be67-4bca-a06b-b7fe1ec8d255 · outbound

This paper cites Scherer and Howard Giles, editors.Social Markers in Speech.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Scherer and Howard Giles, editors.Social Markers in Speech

Reference 5

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source=pdf_text observed=2026-08-02T00:57:37.688134Z digest=sha256:d583607a50ea251dcd56503ee6d5a4974b5dcf98875e1640d175812283904b54

Observation c15afd01-2a8f-4e22-a503-4d7eca91c0f2 · outbound

This paper cites Asimplestsystematicsfortheorganizationofturn-taking for conversation.Language, 50(4):696–735, 1974.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Asimplestsystematicsfortheorganizationofturn-taking for conversation.Language, 50(4):696–735, 1974

Reference 6

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source=pdf_text observed=2026-08-02T00:57:37.692153Z digest=sha256:b6d90d08c6c71710a6a7971311707dc56d83c0a42c30da3d97376c69ec68cdc6

Observation 3673dc5d-8b6a-4ccd-a4f8-79200ba41f61 · outbound

This paper cites doi: 10.1162/tacl.a.628.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems doi: 10.1162/tacl.a.628

Reference 7

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source=pdf_text observed=2026-08-02T00:57:37.696832Z digest=sha256:9fbed254f677bc3498467d9a9333d1f3df9f9c24d8350064d160376ac2102d56

Observation a6e2fb60-11ed-4d4f-b188-75f745b4a8b4 · outbound

This paper cites Speech Robust Bench: A Robustness Benchmark For Speech Recognition.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Speech Robust Bench: A Robustness Benchmark For Speech Recognition

Reference 9

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source=pdf_text observed=2026-08-02T00:57:37.709027Z digest=sha256:87b2368eb792a3e9a5ddfb6c150821edca1e9cc74abbfe56dc31988de4d1f5c6

Observation 370ba510-382f-4f56-a329-7e759a16c3dd · outbound

This paper cites SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation

Reference 10

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source=pdf_text observed=2026-08-02T00:57:37.713378Z digest=sha256:a33b840be80cf1a4d97f327e72fbfe905e30a1d044639ff058c52d5ec1c50c0f

Observation b98dcb16-8a1c-4285-acdc-8fc3449f3b5e · outbound

This paper cites ITU-T Recommendation P.800: Methods for subjective determination of transmission quality.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems ITU-T Recommendation P.800: Methods for subjective determination of transmission quality

Reference 11

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source=pdf_text observed=2026-08-02T00:57:37.718088Z digest=sha256:29b4ad8ef8c63be07e9d582cfa90affd5c56802232cf531a840bbe3480672b34

Observation a74bd06b-2ec2-4ff7-abe6-f9316ec84dfd · outbound

This paper cites Technical Report BS.1534-3, International Telecommunication Union, 2015.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Technical Report BS.1534-3, International Telecommunication Union, 2015

Reference 12

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source=pdf_text observed=2026-08-02T00:57:37.722153Z digest=sha256:cb538e9ec35be6883efc08e7c4e9eb4a4f38191512bc2e366295ff99089df473

Observation 8ff301c5-89f6-443f-a755-57cc200f5090 · outbound

This paper cites Black and Keiichi Tokuda.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Black and Keiichi Tokuda

Reference 13

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doi, observed 2026-08-02T00:58:19.666328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-02T00:57:37.726489Z digest=sha256:9b86b5fa28cc3d292a95cea1cf867a53026883d0ab9e22f3ba4f15d36c6a1958

Observation be10b237-0131-4ea5-8a88-5475f13a684c · outbound

This paper cites The VoiceMOS challenge2022.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems The VoiceMOS challenge2022

Reference 14

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source=pdf_text observed=2026-08-02T00:57:37.730421Z digest=sha256:953e749fb3fe1ccbf6889357261a13dcd3bcdc392acfb53341cac3c684a60061

Observation 14edc465-fbb6-4077-bf96-feaf874c1d18 · outbound

This paper cites The VoiceMOS Challenge 2024: Beyond Speech Quality Prediction.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems The VoiceMOS Challenge 2024: Beyond Speech Quality Prediction

Reference 15

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source=pdf_text observed=2026-08-02T00:57:37.735839Z digest=sha256:47e2fab5e9307fc1792c6378f01d215dde0457c337610f00e564e92f9f9194ee

Observation e2f04486-eb39-4b01-920a-b8217a575738 · outbound

This paper cites InstructTTSEval: Benchmarking Complex Natural-Language Instruction Following in Text-to-Speech Systems.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems InstructTTSEval: Benchmarking Complex Natural-Language Instruction Following in Text-to-Speech Systems

Reference 16

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source=pdf_text observed=2026-08-02T00:57:37.741292Z digest=sha256:c85b4585aebe6072c0aa338c6d6a51746f65a7f5a9bd2148bd288d8019eb1d1b

Observation 15e196c1-fbef-4c31-8ffd-053bb8c9b494 · outbound

This paper cites EmergentTTS-Eval: Evaluating TTS Models on Complex Prosodic, Expressiveness, and Linguistic Challenges Using Model-as-a-Judge.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems EmergentTTS-Eval: Evaluating TTS Models on Complex Prosodic, Expressiveness, and Linguistic Challenges Using Model-as-a-Judge

Reference 17

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source=pdf_text observed=2026-08-02T00:57:37.747884Z digest=sha256:d654ccfa21b61aa2220bbb904c3e86e8912e189341636159baab804798ab1f3e

Observation 93c912b8-80c8-44d5-8ef9-f54a563ddc78 · outbound

This paper cites Walker, Diane J.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Walker, Diane J

Reference 18

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source=pdf_text observed=2026-08-02T00:57:37.752541Z digest=sha256:b6a7e962c5fa0bc948c3f1c9d04337c4490d6b979dabcdf509977a9c6a72923f

Observation 4fd0111f-00eb-4c2b-802b-1c0fb0952540 · outbound

This paper cites SD-Eval: A benchmark dataset for spoken dialogue understanding beyond words.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems SD-Eval: A benchmark dataset for spoken dialogue understanding beyond words

Reference 19

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doi, observed 2026-08-02T00:58:19.508402Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-02T00:57:37.757467Z digest=sha256:06db7c374a38eb39c111c113dbffd1484c5363f948e8e21fc4b3e401dd3b0eb0

Observation d3afbedc-9ce0-42d6-b755-080841e0aa6e · outbound

This paper cites URO-bench: Towards comprehensive evaluation for end-to-end spoken dialogue models.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems URO-bench: Towards comprehensive evaluation for end-to-end spoken dialogue models

Reference 20

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source=pdf_text observed=2026-08-02T00:57:37.761831Z digest=sha256:617ba291b1c2aa226e44b52061b3b8c31c8caf4d667cc17c5eff35b817d629d5

Observation 9c87ecb7-00c4-43c8-acd5-1f69f0890e48 · outbound

This paper cites S2S-Arena: Evaluating Paralinguistic Instruction Following in Speech-to-Speech Models.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems S2S-Arena: Evaluating Paralinguistic Instruction Following in Speech-to-Speech Models

Reference 21

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source=pdf_text observed=2026-08-02T00:57:37.766430Z digest=sha256:9674c13360bac3a976056a9f1fad40a2f9f028a40366ec10ce1a3d46c432deb8

Observation e6968ab4-c770-4d98-a6a2-b6840d29d32b · outbound

This paper cites AIR-bench: Benchmarking large audio-language models via generative comprehension.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems AIR-bench: Benchmarking large audio-language models via generative comprehension

Reference 22

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source=pdf_text observed=2026-08-02T00:57:37.771079Z digest=sha256:b675434add62b76381c8c134c314b13063260646bb82ee08c401a76c2495d061

Observation b5df8b96-0cff-4df8-88c7-270d43f3cb9b · outbound

This paper cites an unresolved cited work.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Unresolved cited work

Reference 23

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source=pdf_text observed=2026-08-02T00:57:37.776244Z digest=sha256:1eaa88a320e0c53ab32c957ad4c8dc0ebad82cd6ea1bebb2acd082eb8bb3a0fd

Observation 6d5d8abb-cfc8-46d0-a5f4-cc38b5f97d4c · outbound

This paper cites AHELM: A Holistic Evaluation of Audio-Language Models.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems AHELM: A Holistic Evaluation of Audio-Language Models

Reference 24

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source=pdf_text observed=2026-08-02T00:57:37.780637Z digest=sha256:6213ce29354ebb7bd057a71d605b7f9f6fe78aa1640fd2bdc0873cbcd01fb02f

Observation 7cef86de-f4a5-453e-a6cb-b616f6070aa1 · outbound

This paper cites Common voice: A massively-multilingual speech corpus.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Common voice: A massively-multilingual speech corpus

Reference 25

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source=pdf_text observed=2026-08-02T00:57:37.784998Z digest=sha256:e8a4f5034a0b2890592973235838180891111f5059620a7c7535c5f7e73e6362

Observation b6251651-10f3-4979-82c9-c60ff56d9ee3 · outbound

This paper cites FLEURS: Few-shot learning evaluation of universal representations of speech.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems FLEURS: Few-shot learning evaluation of universal representations of speech

Reference 26

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source=pdf_text observed=2026-08-02T00:57:37.790343Z digest=sha256:f51d7413e999efb7807169cf2122c429af6f6ec54599de2bc589c4eea25dedd0

Observation d55737d4-8200-400b-b809-9fa79a6a4da5 · outbound

This paper cites CHiME-6 challenge: Tackling multispeaker speech recognition for unsegmented recordings.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems CHiME-6 challenge: Tackling multispeaker speech recognition for unsegmented recordings

Reference 27

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source=pdf_text observed=2026-08-02T00:57:37.794744Z digest=sha256:cdd9653e847e1fa756411cb3df0cf543eff02ae8a77cc47d23669995e5ae44be

Observation b5832878-a711-4d29-95c1-204764ce164e · outbound

This paper cites Librispeech: An asr corpus based on public domain audio books.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Librispeech: An asr corpus based on public domain audio books

Reference 28

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source=pdf_text observed=2026-08-02T00:57:37.798883Z digest=sha256:c35ce1264062c1223457ca74a2171cacf9d4b9c9c460e8662a663060ad3fd1b3

Observation cf15931f-eef3-4d7a-acdc-55886c102da3 · outbound

This paper cites Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation

Reference 29

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source=pdf_text observed=2026-08-02T00:57:37.802866Z digest=sha256:c4908430f68214724eb3c79e689a39226313403d5910ca3767c4ef37c3a87d56

Observation e3fa44c7-6d19-4263-8be7-a8a649e5d4f6 · outbound

This paper cites Open asr leaderboard: Towards reproducible and transparent multilingual and long-form speech recognition evaluation, 2025.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Open asr leaderboard: Towards reproducible and transparent multilingual and long-form speech recognition evaluation, 2025

Reference 30

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T00:57:37.807408Z digest=sha256:753ae1f35f9696a05a36d54a28208d6755d83d594679c0712529d1154f31b27c

Observation 80dc10bc-3e3e-4c2f-875e-ce69fcc53c6a · outbound

This paper cites an unresolved cited work.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Unresolved cited work

Reference 31

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verified exact
doi, observed 2026-08-02T00:58:19.347366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-02T00:57:37.812119Z digest=sha256:5cb9a1a092f7d1789639aabf691c116cd271871ed79ad00bdc6eadb50a69a8e3

Observation 8967ab2e-d5bf-45b1-ab9f-e65f1ee5ae71 · outbound

This paper cites BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition

Reference 32

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source=pdf_text observed=2026-08-02T00:57:37.818199Z digest=sha256:2c87ecf30fc55dfb388c47ef5314bfa8304a1675da43683cb47864566e08266a

Observation f5f2c3cc-16b1-4558-8bd9-8d6316715ef4 · outbound

This paper cites A female speaker delivers a clear, expressive speech in a quiet, high- quality recording.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems A female speaker delivers a clear, expressive speech in a quiet, high- quality recording

Reference 33

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source=pdf_text observed=2026-08-02T00:57:37.822940Z digest=sha256:8c05d80245208aa5a2e3820918d8680541778052ee407e01d537da9220061825

Observation c43baa13-8985-4d58-8d2b-07e11974c5b5 · outbound

This paper cites an unresolved cited work.

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems Unresolved cited work

Reference 2025

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source=pdf_text observed=2026-08-02T00:57:37.704890Z digest=sha256:ff249ee9d8715a25de1f3fae008c348c8f956dbcb600c92543ea91203a445d70

Pith citing papers

Observation 37dacbd7-18fd-44da-b3df-5ba1803f8db5 · inbound

CallScreenBench: Benchmarking On-Device Models as Phone Secretaries cites this paper.

CallScreenBench: Benchmarking On-Device Models as Phone Secretaries RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems

Reference 10

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local_arxiv, observed 2026-08-06T00:42:04.264356Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T00:41:57.270130Z digest=sha256:f71837b2d5715fe6a358ca9ac44e457315ff7a50b81722b4c400c345a5657aeb