Paper Citation Record · LEDGER

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition

As of 22 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:1908.05227.

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

pith.paper-citation-record.v1
1908.05227 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:24:40.258828Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:24:40.161149Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T14:24:40.304973Z

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy19
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 34a42d0d-0071-4a02-a4ef-de9c9d7c3056 · outbound

This paper cites Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T14:24:40.310655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a4506887-9fb2-4cd9-871b-d4f0f316e616 · outbound

This paper cites An alternative technique, re- ferred to as end-to-end, aims to learn the mapping from acoustic features to text directly without the need of intermediate steps.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition An alternative technique, re- ferred to as end-to-end, aims to learn the mapping from acoustic features to text directly without the need of intermediate steps

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.553837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3e92d11f-df6f-43f3-b879-e2e9e364a21a · outbound

This paper cites an unresolved cited work.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-14T14:24:40.544633Z

Source-reported events for the cited work

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Observation 28c24970-2e7b-4e2a-8203-7f20f69c3e99 · outbound

This paper cites an unresolved cited work.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-14T14:24:40.535007Z

Source-reported events for the cited work

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Observation b06f6b36-e7f2-4c9d-af6b-bcbc71826420 · outbound

This paper cites an unresolved cited work.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-14T14:24:40.526272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 79db2310-abdb-4a6a-9eb9-4105e64f1503 · outbound

This paper cites an unresolved cited work.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-14T14:24:40.517178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c0b1ba35-b3f5-46d7-8c48-eaa000fdb241 · outbound

This paper cites Experiments are performed on TEDLIUM and Table 1: Training, adaptation and test data for different dataset.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Experiments are performed on TEDLIUM and Table 1: Training, adaptation and test data for different dataset

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.507297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 696375cb-c385-45ea-b724-c0bce53a37a3 · outbound

This paper cites The following ASR systems will be analyzed: • LF-MMI: This ASR refers to the traditional chain model using LF-MMI optimization criteria.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition The following ASR systems will be analyzed: • LF-MMI: This ASR refers to the traditional chain model using LF-MMI optimization criteria

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.498102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7a1c058e-7600-40d1-921c-8295e851eed9 · outbound

This paper cites For exploiting unlabelled data, the base- line system employs a single best hypothesized text-transcript.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition For exploiting unlabelled data, the base- line system employs a single best hypothesized text-transcript

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.488226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 72480f16-53bb-49f2-8d30-22eb6499258a · outbound

This paper cites SM2 - Extracting semantic meaning from spoken material.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition SM2 - Extracting semantic meaning from spoken material

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.478477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 05176ac8-633e-43e0-8716-040c8606e2a0 · outbound

This paper cites Deep neural networks for acoustic modeling in speech recognition,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Deep neural networks for acoustic modeling in speech recognition,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.469206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d98ed778-3ed6-4203-ba36-2915df1427fb · outbound

This paper cites New types of deep neural network learning for speech recognition and related applications: An overview,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition New types of deep neural network learning for speech recognition and related applications: An overview,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.459617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 72f2f636-0075-45f8-b6a6-f138cf5ce715 · outbound

This paper cites Purely sequence-trained neu- ral networks for asr based on lattice-free mmi.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Purely sequence-trained neu- ral networks for asr based on lattice-free mmi

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.449879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 97140727-021b-46da-ac93-23b8ca1f4a79 · outbound

This paper cites Exploiting foreign resources for dnn-based asr,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Exploiting foreign resources for dnn-based asr,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.439520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b9135fd7-2632-4381-ade0-1416871ef5ef · outbound

This paper cites Joint ctc-attention based end-to-end speech recognition using multi-task learning,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Joint ctc-attention based end-to-end speech recognition using multi-task learning,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T14:24:40.210964Z

Source-reported events for the cited work

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Observation f64c8e50-6967-4ad8-97fc-5c3b8531561c · outbound

This paper cites ESPnet: End-to-End Speech Processing Toolkit.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition ESPnet: End-to-End Speech Processing Toolkit

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T14:24:40.214331Z

Source-reported events for the cited work

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Observation 326409c8-81aa-4311-b3c3-9a5761498984 · outbound

This paper cites Eesen: End-to-end speech recognition using deep rnn models and wfst-based decoding,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Eesen: End-to-end speech recognition using deep rnn models and wfst-based decoding,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.422675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4227d23d-e083-4bf9-ba59-10b03810e01f · outbound

This paper cites Semi- supervised training of acoustic models using lattice-free mmi,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Semi- supervised training of acoustic models using lattice-free mmi,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.411964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fcc3294a-d0ed-4bad-a3ba-bd5ef7b8e881 · outbound

This paper cites Semi-supervised end-to-end speech recognition,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Semi-supervised end-to-end speech recognition,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.402798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e1d2d826-9adc-4e1b-b11c-66b86888f455 · outbound

This paper cites Pseudo-label: The simple and efﬁcient semi- supervised learning method for deep neural networks,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Pseudo-label: The simple and efﬁcient semi- supervised learning method for deep neural networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.393446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6a8ba271-431e-49ba-b8be-14cace826ece · outbound

This paper cites Semi-Supervised Model Training for Unbounded Conversational Speech Recognition.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Semi-Supervised Model Training for Unbounded Conversational Speech Recognition

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T14:24:40.230291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd04d784-46dd-4645-9ac1-fc2aebed5815 · outbound

This paper cites Learning with pseudo- ensembles,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Learning with pseudo- ensembles,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.383504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ac9bc6e5-b49c-4514-891b-984c83ffbc35 · outbound

This paper cites Dropout: A simple way to prevent neural networks from overﬁtting,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Dropout: A simple way to prevent neural networks from overﬁtting,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T14:24:40.237685Z

Source-reported events for the cited work

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Observation 03a6b331-57cb-47b0-ad1d-ff16b6ee7336 · outbound

This paper cites Analyzing uncer- tainties in speech recognition using dropout,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Analyzing uncer- tainties in speech recognition using dropout,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.366359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cc754603-bb6b-4f66-886a-7089eacf2467 · outbound

This paper cites Dropout as a bayesian approxima- tion: Representing model uncertainty in deep learning,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Dropout as a bayesian approxima- tion: Representing model uncertainty in deep learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.356089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 54f4e272-9b97-45af-8f5a-91c022bd188b · outbound

This paper cites End-to-end speech recogni- tion with word-based rnn language models,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition End-to-end speech recogni- tion with word-based rnn language models,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.346212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fa139695-5ee4-429f-b444-e7c5613dcc25 · outbound

This paper cites Lib- rispeech: an asr corpus based on public domain audio books,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Lib- rispeech: an asr corpus based on public domain audio books,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.336161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0c3ce407-e2ba-484d-a0ad-69227bee21ca · outbound

This paper cites Ted-lium: an automatic speech recognition dedicated corpus.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition Ted-lium: an automatic speech recognition dedicated corpus

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:40.326669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 27d639b6-e898-4444-b7a7-4ab78e3a77c7 · outbound

This paper cites The kaldi speech recognition toolkit,.

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition The kaldi speech recognition toolkit,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-14T14:24:40.258828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 34a42d0d-0071-4a02-a4ef-de9c9d7c3056 · inbound