Paper Citation Record · LEDGER

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures

As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 3 inbound Pith citation observations for arXiv:2507.05885.

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

pith.paper-citation-record.v1
2507.05885 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:23:37.327949Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:23:32.633803Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T05:51:10.230261Z

Reference resolution

50 of 50 outbound references displayed

verified exact1
verified fuzzy45
unresolved4
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 63129485-d203-472c-818e-095696e3ba90 · outbound

This paper cites Large datasets and advances in deep learning have significantly improved the per- formance of speech technologies [2, 3].

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Large datasets and advances in deep learning have significantly improved the per- formance of speech technologies [2, 3]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:47.696663Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 75489d3a-bcf6-4718-bf1b-9a265b8d65a2 · outbound

This paper cites How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures

Reference 2

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Source-reported events for the cited work

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Observation 86f85bac-4060-46b7-bd62-c781d272bebc · outbound

This paper cites The Dutch Corpora We use the Corpus Gesproken Nederlands (CGN) [38], which consists of speech spoken by Dutch adult, native speakers.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures The Dutch Corpora We use the Corpus Gesproken Nederlands (CGN) [38], which consists of speech spoken by Dutch adult, native speakers

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:47.206913Z

Source-reported events for the cited work

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Observation f4455ece-cb45-4af8-a58b-5220e4146acd · outbound

This paper cites 4.1), followed by bias measures evalua- tion (Sec.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures 4.1), followed by bias measures evalua- tion (Sec

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:47.042341Z

Source-reported events for the cited work

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Observation c4260c70-bc8a-40d7-8553-a1218cc4cfc5 · outbound

This paper cites In line with the potential pitfalls, there is a clear need for performance and bias measures to capture performance variation, and this paper gives recommendations on it.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures In line with the potential pitfalls, there is a clear need for performance and bias measures to capture performance variation, and this paper gives recommendations on it

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:46.681706Z

Source-reported events for the cited work

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Observation 30808f95-b14f-4d9c-885b-83b3db56e52f · outbound

This paper cites The accent gap,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures The accent gap,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:45.706570Z

Source-reported events for the cited work

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Observation c61f40d4-f969-4b0b-9cee-24986a5b3543 · outbound

This paper cites Also the overall bias mea- sures capture the earlier findings that the mitigation approaches do not reduce bias despite improving performance.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Also the overall bias mea- sures capture the earlier findings that the mitigation approaches do not reduce bias despite improving performance

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:46.877913Z

Source-reported events for the cited work

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Observation ed004d01-f7a7-4177-ab68-4a55da317289 · outbound

This paper cites Google’s speech recognition has a gender bias,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Google’s speech recognition has a gender bias,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:45.217034Z

Source-reported events for the cited work

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Observation 6e8d24f0-1107-4ff6-b3fd-601134947fd0 · outbound

This paper cites V oice in human-agent interaction: A survey,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures V oice in human-agent interaction: A survey,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:46.482585Z

Source-reported events for the cited work

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Observation 21c4a650-f325-4a58-b960-1934a317adf2 · outbound

This paper cites Speech recognition in our every- day life,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Speech recognition in our every- day life,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:46.283940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f2db4d1c-b6f0-4d50-ac6b-4132f16f3068 · outbound

This paper cites A review of deep learning techniques for speech processing,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures A review of deep learning techniques for speech processing,

Reference 11

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Source-reported events for the cited work

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Observation fe4a0199-cb00-4872-8f35-624659b87550 · outbound

This paper cites SUPERB: Speech Processing Universal PER- formance Benchmark,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures SUPERB: Speech Processing Universal PER- formance Benchmark,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:46.077898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2e661491-ed7f-4977-8708-c2684f7020cf · outbound

This paper cites To- wards inclusive automatic speech recognition,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures To- wards inclusive automatic speech recognition,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:45.903831Z

Source-reported events for the cited work

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Observation a1aed0e0-e5cf-4991-ba09-0a189f5d2a13 · outbound

This paper cites A Survey on Bias and Fairness in Machine Learning.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures A Survey on Bias and Fairness in Machine Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:23:34.529168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afd3be4f-1c56-46ff-8ca4-ed5867aa34a8 · outbound

This paper cites Racial disparities in automated speech recog- nition,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Racial disparities in automated speech recog- nition,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:45.475738Z

Source-reported events for the cited work

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Observation b62fc239-9871-4243-85fc-637ab5956c70 · outbound

This paper cites De-biasing “bias.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures De-biasing “bias

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:43.938828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ad5291a3-11b6-4a95-b605-a80d4225f74e · outbound

This paper cites V oice recognition still has signifi- cant race and gender biases,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures V oice recognition still has signifi- cant race and gender biases,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:44.986783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d2683f73-231a-48d1-9915-cb01638b6585 · outbound

This paper cites Speech recognition tech is yet another example of bias,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Speech recognition tech is yet another example of bias,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:44.744932Z

Source-reported events for the cited work

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Observation 0a140664-8064-47e7-9dce-b855a3305ae6 · outbound

This paper cites Toward Fairness in Speech Recognition: Dis- covery and mitigation of performance disparities,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Toward Fairness in Speech Recognition: Dis- covery and mitigation of performance disparities,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:44.571265Z

Source-reported events for the cited work

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Observation ce011953-ad78-409f-a0c4-7d76a322dc52 · outbound

This paper cites An overview of noise-robust automatic speech recognition,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures An overview of noise-robust automatic speech recognition,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:44.331815Z

Source-reported events for the cited work

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Observation 7d3e9e98-d716-4127-a8b5-25de2e9aa3d0 · outbound

This paper cites Effects of talker dialect, gender & race on accuracy of bing speech and youtube automatic captions,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Effects of talker dialect, gender & race on accuracy of bing speech and youtube automatic captions,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:44.148819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fe4bd98b-e042-4a6e-a99e-07ed0e7855ac · outbound

This paper cites Disorders of communication: Dysarthria,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Disorders of communication: Dysarthria,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:42.556155Z

Source-reported events for the cited work

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Observation a03ab24d-d0d1-4280-a41f-d9ac642bd920 · outbound

This paper cites Quantifying Bias in Automatic Speech Recognition.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Quantifying Bias in Automatic Speech Recognition

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T19:23:34.623796Z

Source-reported events for the cited work

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Observation 86732ac7-a57b-4a80-833a-68b9446750c0 · outbound

This paper cites The ordering of milestones in language development for children from 1 to 6 years of age,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures The ordering of milestones in language development for children from 1 to 6 years of age,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:42.058377Z

Source-reported events for the cited work

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Observation 4a6b7785-77da-4ad4-b44f-68670d00e9aa · outbound

This paper cites The development of gen- dered speech in children: Insights from adult L1 and L2 percep- tions,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures The development of gen- dered speech in children: Insights from adult L1 and L2 percep- tions,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:43.732408Z

Source-reported events for the cited work

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Observation 021fc248-8ebd-4756-99ee-49a01716433c · outbound

This paper cites Whats special in a child’s larynx?.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Whats special in a child’s larynx?

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:43.486634Z

Source-reported events for the cited work

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Observation 391ed9f4-f440-432b-852c-ab6839cc9100 · outbound

This paper cites Acoustics of children’s speech: Developmental changes of temporal and spectral parame- ters,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Acoustics of children’s speech: Developmental changes of temporal and spectral parame- ters,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:43.280664Z

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Observation 93d4b511-4465-46b2-b27c-4b4dc77f8e89 · outbound

This paper cites Male and female speech: a study of mean f0, f0 range, phonation type and speech rate in Parisian French and Ameri- can English speakers,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Male and female speech: a study of mean f0, f0 range, phonation type and speech rate in Parisian French and Ameri- can English speakers,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:43.051699Z

Source-reported events for the cited work

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Observation 6e53aa6b-da19-4b20-8375-d61760265e9e · outbound

This paper cites Differences in voice quality between men and women: Use of the long-term average spectrum (LTAS),.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Differences in voice quality between men and women: Use of the long-term average spectrum (LTAS),

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:42.801777Z

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Observation 56769453-1576-4a24-9687-23b1f14edc2b · outbound

This paper cites Both [5, 27] found speech type to impact ASR per- formance with read speech being favored over non-read speech.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Both [5, 27] found speech type to impact ASR per- formance with read speech being favored over non-read speech

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:47.521070Z

Source-reported events for the cited work

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Observation b0ec64d1-aec6-4053-9995-9e8b057c650f · outbound

This paper cites Casual Conversations (CC).

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Casual Conversations (CC)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:47.355783Z

Source-reported events for the cited work

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Observation 02ac00c5-5fa7-47ad-8e04-256ab64e016e · outbound

This paper cites Studying language, culture, and society: Sociolinguistics or linguistic anthropology,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Studying language, culture, and society: Sociolinguistics or linguistic anthropology,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:42.333164Z

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Observation 85d55982-4b9f-4253-b246-a9eb863366ac · outbound

This paper cites The production of “new.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures The production of “new

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:41.793163Z

Source-reported events for the cited work

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Observation d83eb213-4fa6-4f80-aedb-ef8057301166 · outbound

This paper cites Gender and Dialect Bias in YouTube’s Automatic Captions,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Gender and Dialect Bias in YouTube’s Automatic Captions,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:41.552663Z

Source-reported events for the cited work

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Observation f0c66cdf-09d4-4168-93ba-193900bfa9bf · outbound

This paper cites Gender representation in French broadcast corpora and its impact on ASR performance,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Gender representation in French broadcast corpora and its impact on ASR performance,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:41.297237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3e423f5e-4f69-4d08-b4e8-5acd8c82baa8 · outbound

This paper cites Investigating the Impact of Gender Representation in ASR Training Data: a Case Study on Librispeech,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Investigating the Impact of Gender Representation in ASR Training Data: a Case Study on Librispeech,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:41.030506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1f1d311b-fc2e-4ebe-ace4-8dc7d11345e2 · outbound

This paper cites Seamless equal accuracy ratio for inclusive CTC speech recognition,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Seamless equal accuracy ratio for inclusive CTC speech recognition,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:40.807497Z

Source-reported events for the cited work

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Observation a137f39f-3924-4100-8f89-7e29972bc763 · outbound

This paper cites Training and typological bias in ASR performance for world Englishes,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Training and typological bias in ASR performance for world Englishes,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:40.563340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3079cd59-e162-4e62-b047-cb8ca6df4cda · outbound

This paper cites Towards measuring fairness in speech recognition: Casual Conversations dataset transcriptions,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Towards measuring fairness in speech recognition: Casual Conversations dataset transcriptions,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:40.340649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 27ddcfa1-faf3-4fd3-9364-dcaea82492e5 · outbound

This paper cites Model-based approach for measuring the fairness in ASR,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Model-based approach for measuring the fairness in ASR,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:40.064350Z

Source-reported events for the cited work

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Observation f95ce24d-badb-454e-9306-2b34cddf366b · outbound

This paper cites Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:23:37.549873Z

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Observation 7fea45fc-a127-48b8-9a46-d6bb7780061a · outbound

This paper cites Mitigating bias against non-native accents,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Mitigating bias against non-native accents,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:39.771205Z

Source-reported events for the cited work

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Observation 750d62a0-a75c-4562-bd4b-32b9e0cb51bd · outbound

This paper cites Mitigating regional accent bias in asr systems,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Mitigating regional accent bias in asr systems,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:39.578465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8be07d1c-3fdd-4c10-b903-95bebd033857 · outbound

This paper cites Compar- ing data augmentation and training techniques to reduce bias against non-native accents in hybrid speech recognition systems,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Compar- ing data augmentation and training techniques to reduce bias against non-native accents in hybrid speech recognition systems,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:39.327292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4e6d9adf-8f32-4911-9366-cc0be8dddfaf · outbound

This paper cites Exploring data augmentation in bias mitigation against non- native-accented speech,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Exploring data augmentation in bias mitigation against non- native-accented speech,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:39.097585Z

Source-reported events for the cited work

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Observation f16d66e8-b19c-4582-8688-13fcd8488822 · outbound

This paper cites The Spoken Dutch Corpus. Overview and First Evaluation,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures The Spoken Dutch Corpus. Overview and First Evaluation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:38.835864Z

Source-reported events for the cited work

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Observation b501aabc-486d-481f-93d9-d47bf8326337 · outbound

This paper cites Jasmin-CGN: Extension of the spoken Dutch corpus with speech of elderly people, children and non-natives in the human-machine interaction modality,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Jasmin-CGN: Extension of the spoken Dutch corpus with speech of elderly people, children and non-natives in the human-machine interaction modality,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:38.606816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ca666fd1-2e8c-4314-919a-06cfe62e50a6 · outbound

This paper cites Conformer: Convolution-augmented transformer for speech recognition,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Conformer: Convolution-augmented transformer for speech recognition,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:38.297534Z

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Observation bfe30817-198c-4259-97b8-cde5b6bc7cb9 · outbound

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

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures Robust speech recognition via large-scale weak su- pervision,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:38.021838Z

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Observation 9e634be7-be09-46ab-a3ac-a2b8e6a26d81 · outbound

This paper cites ESPnet: End-to-End speech processing toolkit,.

How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures ESPnet: End-to-End speech processing toolkit,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:37.827155Z

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Pith citing papers

Observation 75489d3a-bcf6-4718-bf1b-9a265b8d65a2 · inbound