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Paper Citation Record · LEDGER

Identifying and Calibrating Overconfidence in Noisy Speech Recognition

As of 13 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations for arXiv:2509.07195.

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

pith.paper-citation-record.v1
2509.07195 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:44:25.131506Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T08:54:56.253187Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T08:55:34.712184Z

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d2f0c863-d086-4d31-9902-82cba5c1a2e9 · outbound

This paper cites Confidence measures for speech recognition: A survey,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Confidence measures for speech recognition: A survey,

Reference 1

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Observation 1da35044-4d14-4781-8f98-74aaf543bbcf · outbound

This paper cites Evaluating OpenAI’s Whisper ASR: Perfor- mance analysis across diverse accents and speaker traits,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Evaluating OpenAI’s Whisper ASR: Perfor- mance analysis across diverse accents and speaker traits,

Reference 2

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Observation 53d015ce-d974-4eb9-8df9-ab2a0aa0effd · outbound

This paper cites Speech recognition in adverse conditions by humans and machines,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Speech recognition in adverse conditions by humans and machines,

Reference 3

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Observation 0c378da7-d6af-4eae-a3b5-e7c8f0057cb5 · outbound

This paper cites Release from same-talker speech-in-speech masking: Effects of masker intelligibility and other contributing factors,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Release from same-talker speech-in-speech masking: Effects of masker intelligibility and other contributing factors,

Reference 4

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Observation 810075d1-69aa-4ca0-8347-df82bb7d614e · outbound

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

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Robust speech recognition via large-scale weak supervi- sion,

Reference 5

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Observation c9518324-b963-4048-9122-73c1a4bf2967 · outbound

This paper cites Evaluation of a noise-robust dsr front-end on aurora databases.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Evaluation of a noise-robust dsr front-end on aurora databases

Reference 6

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Observation c6effed1-3918-4cd2-94ea-af48da200399 · outbound

This paper cites A minimum- mean-square-error noise reduction algorithm on mel-frequency cepstra for robust speech recognition,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition A minimum- mean-square-error noise reduction algorithm on mel-frequency cepstra for robust speech recognition,

Reference 7

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Observation 15f0e9fd-f76b-4fb5-8fd0-df083a80ee30 · outbound

This paper cites An investigation of deep neural net- works for noise robust speech recognition,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition An investigation of deep neural net- works for noise robust speech recognition,

Reference 8

Resolution
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Observation 1adff0b3-6cd3-4b6d-bc71-5fe962850c02 · outbound

This paper cites Audio augmentation for speech recognition.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Audio augmentation for speech recognition

Reference 9

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Observation f1ccc55c-c23a-4dec-be40-8b249925fa0e · outbound

This paper cites SpecAugment: A simple data augmentation method for automatic speech recognition,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition SpecAugment: A simple data augmentation method for automatic speech recognition,

Reference 10

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Observation ac0044df-8376-4b0d-b02f-157fd735d014 · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 11

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Observation ec0e2e78-82cf-47b7-a9de-566cf96180a6 · outbound

This paper cites Speech en- hancement and recognition using multi-task learning of long short-term memory recurrent neural networks.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Speech en- hancement and recognition using multi-task learning of long short-term memory recurrent neural networks

Reference 12

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Observation 98304263-7f13-4b06-b050-39427a9fd317 · outbound

This paper cites Improved estimation, evaluation and applications of confidence measures for speech recognition.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Improved estimation, evaluation and applications of confidence measures for speech recognition

Reference 13

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Observation c6198890-712c-4b51-8f97-240c4ad5af71 · outbound

This paper cites Posterior probability decoding, confi- dence estimation and system combination,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Posterior probability decoding, confi- dence estimation and system combination,

Reference 14

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Observation 3a06eb62-3358-43b0-ac99-5ea37e12eb9a · outbound

This paper cites Confidence measures for large vocabulary continuous speech recognition,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Confidence measures for large vocabulary continuous speech recognition,

Reference 15

Resolution
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Observation 0d59569e-a220-49df-a211-6a043783d6ae · outbound

This paper cites High-level approaches to confidence estimation in speech recognition,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition High-level approaches to confidence estimation in speech recognition,

Reference 16

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Observation 02522c72-bc47-4906-882d-b8c7584e22a5 · outbound

This paper cites Combining information sources for confidence estimation with crf models.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Combining information sources for confidence estimation with crf models

Reference 17

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Observation f09b2e3b-e391-4efe-bd69-39167260e1a5 · outbound

This paper cites Calibration of confidence measures in speech recognition,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Calibration of confidence measures in speech recognition,

Reference 18

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This paper cites On calibration of modern neural networks,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition On calibration of modern neural networks,

Reference 19

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Observation 49b1a461-ced9-4ba5-8845-d7ecb0f569d6 · outbound

This paper cites Improving ASR confidence scores for Alexa using acoustic and hy- pothesis embeddings,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Improving ASR confidence scores for Alexa using acoustic and hy- pothesis embeddings,

Reference 20

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Observation b065b23e-344b-40f0-b473-3a05468fbefc · outbound

This paper cites Confidence measures in encoder-decoder models for speech recognition.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Confidence measures in encoder-decoder models for speech recognition

Reference 21

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Observation f964804a-b6ad-4c51-b5e5-217a1caab393 · outbound

This paper cites Confidence estimation for attention-based sequence-to- sequence models for speech recognition,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Confidence estimation for attention-based sequence-to- sequence models for speech recognition,

Reference 22

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Observation b1d96f42-251a-4168-ab6b-9aba5303b142 · outbound

This paper cites Learning word-level confidence for subword end- to-end asr,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Learning word-level confidence for subword end- to-end asr,

Reference 23

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Observation f7b2ee62-8d3a-402a-b6fa-902226c21800 · outbound

This paper cites Blstm-based confi- dence estimation for end-to-end speech recognition,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Blstm-based confi- dence estimation for end-to-end speech recognition,

Reference 24

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This paper cites Multi-task learning for end-to-end asr word and utterance confidence with deletion prediction,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Multi-task learning for end-to-end asr word and utterance confidence with deletion prediction,

Reference 25

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Identifying and Calibrating Overconfidence in Noisy Speech Recognition Adopting Whisper for confidence estimation,

Reference 26

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Observation 8ae41590-03cf-4765-b8ad-338011871eb8 · outbound

This paper cites Improving confidence estimation on out-of-domain data for end-to-end speech recognition,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Improving confidence estimation on out-of-domain data for end-to-end speech recognition,

Reference 27

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This paper cites Fast entropy-based methods of word-level confidence estimation for end-to-end automatic speech recognition,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Fast entropy-based methods of word-level confidence estimation for end-to-end automatic speech recognition,

Reference 28

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This paper cites Utterance-level neural confidence measure for end- to-end children speech recognition,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Utterance-level neural confidence measure for end- to-end children speech recognition,

Reference 29

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Observation 97816461-8799-4105-a3c5-e6489dc6e3a4 · outbound

This paper cites Proper error estimation and calibration for attention-based encoder-decoder models,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Proper error estimation and calibration for attention-based encoder-decoder models,

Reference 30

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This paper cites Calibration of pre-trained transformers,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Calibration of pre-trained transformers,

Reference 31

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This paper cites An evaluation of word- level confidence estimation for end-to-end automatic speech recogni- tion,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition An evaluation of word- level confidence estimation for end-to-end automatic speech recogni- tion,

Reference 32

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This paper cites Calibrated Selective Classification.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Calibrated Selective Classification

Reference 33

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Observation 1dd44ee4-027d-4eb3-8115-b7c16863f70d · outbound

This paper cites Improving Predictor Reliability with Selective Recalibration.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Improving Predictor Reliability with Selective Recalibration

Reference 34

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Observation 7aaaa495-893b-44ed-a2a9-516072eb174a · outbound

This paper cites LibriTTS: A corpus derived from librispeech for text-to-speech,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition LibriTTS: A corpus derived from librispeech for text-to-speech,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:44:25.208823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-04T22:44:25.122110Z digest=sha256:52728b60049308585f1543d0a31d87d4d6d0ef72e15417cef0b6befcbdbc8cae

Observation 582dede4-f352-459a-aaa7-a347620de87e · outbound

This paper cites The revised speech perception in noise test (R-SPIN) in a multiple signal-to-noise ratio paradigm,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition The revised speech perception in noise test (R-SPIN) in a multiple signal-to-noise ratio paradigm,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:44:25.200353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-04T22:44:25.124462Z digest=sha256:799c8a4e6ed07a28ae745e96316eca4b49b91140630f61d49fd4bcfbf91c97b7

Observation d1b4ce1b-bd7d-4fb9-a6ff-a11ba56d93df · outbound

This paper cites From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:44:25.191251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-04T22:44:25.126889Z digest=sha256:ca64c5b46665189cf955be620207391446867a0d7aad935dd161534f4fecf671

Observation d87f5302-86bc-4d27-aadd-1df9310ea29e · outbound

This paper cites Soft calibration objectives for neural networks,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Soft calibration objectives for neural networks,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:44:25.183562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-04T22:44:25.129151Z digest=sha256:c27ac26cc4770c9320bfb9e7b78a9c6069f3eb0d0082f3e2b11260c677df680a

Observation 6bb375e7-9725-4d8c-aab3-4aed978dbea0 · outbound

This paper cites Access: Advancing innovation: Nsf’s advanced cyberinfrastructure co- ordination ecosystem: Services & support,.

Identifying and Calibrating Overconfidence in Noisy Speech Recognition Access: Advancing innovation: Nsf’s advanced cyberinfrastructure co- ordination ecosystem: Services & support,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:44:25.174929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-04T22:44:25.131506Z digest=sha256:b79f732c302fd8189cbe2d06f362c886d72fb06f70a7afd716fd83a206b80807

Pith citing papers

Observation 34620f83-72d2-42d9-ae56-d62c19d74753 · inbound

RAS: a Reliability Oriented Metric for Automatic Speech Recognition cites this paper.

RAS: a Reliability Oriented Metric for Automatic Speech Recognition Identifying and Calibrating Overconfidence in Noisy Speech Recognition

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:16:14.131914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-07T17:53:51.401106Z digest=sha256:8bf30d66fba4d0bdbbacaebdaacda315fa7fbb5999750bbdee42913c9d6bfc6f

Observation 56a771c9-1bae-49c8-bc6d-30d924b9f279 · inbound

RAS: a Reliability Oriented Metric for Automatic Speech Recognition cites this paper.

RAS: a Reliability Oriented Metric for Automatic Speech Recognition Identifying and Calibrating Overconfidence in Noisy Speech Recognition

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-07-01T08:55:34.713762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-07-01T08:54:56.253187Z digest=sha256:ba57bf9b5d28d9c73e7b01515b907fc539a8ead420fc02d9604cfc2a6f0d817d