Paper Citation Record · LEDGER

Teach an all-rounder with experts in different domains

As of 21 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:1907.05698.

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

pith.paper-citation-record.v1
1907.05698 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T00:03:54.848638Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T00:03:54.848638Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-25T00:05:06.691426Z

Reference resolution

31 of 31 outbound references displayed

verified exact2
verified fuzzy24
unresolved1
parse uncertain0
malformed identifier4
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d625403a-032d-4c2b-a6a0-f6880c574f3a · outbound

This paper cites Teach an all-rounder with experts in different domains.

Teach an all-rounder with experts in different domains Teach an all-rounder with experts in different domains

Reference 1

Resolution: malformed identifier; local_arxiv, observed 2026-05-25T00:05:06.696424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9b2fec71-d2f7-4fce-a807-de5867c6321c · outbound

This paper cites an unresolved cited work.

Teach an all-rounder with experts in different domains Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-05-25T00:05:07.898265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 830d9e5f-3635-4c7e-8e84-f6536536fd67 · outbound

This paper cites Dn denotes the n-th domain.

Teach an all-rounder with experts in different domains Dn denotes the n-th domain

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.902613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0594c593-d73a-4bae-ae1c-3f774beba866 · outbound

This paper cites Tn denotes the n-th teacher model which is trained with then-th domain data.

Teach an all-rounder with experts in different domains Tn denotes the n-th teacher model which is trained with then-th domain data

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.910781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b2180420-49ab-4867-ab40-c9dd103228d4 · outbound

This paper cites During the training process, sam- ples in one minibatch are chosen randomly from the mixed data set, and may come from different domains.

Teach an all-rounder with experts in different domains During the training process, sam- ples in one minibatch are chosen randomly from the mixed data set, and may come from different domains

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.892834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a2a631d4-5083-4520-938e-896813dc31c8 · outbound

This paper cites Training setup The feature vectors used in all the experiments are 40- dimensional log-mel ﬁlterbank energy features appended with the ﬁrst and second order derivatives.

Teach an all-rounder with experts in different domains Training setup The feature vectors used in all the experiments are 40- dimensional log-mel ﬁlterbank energy features appended with the ﬁrst and second order derivatives

Reference 6

Resolution: malformed identifier; raw_fallback, observed 2026-05-25T00:05:07.881340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0726aeb5-02a3-46a8-8ee5-058f9818aafd · outbound

This paper cites an unresolved cited work.

Teach an all-rounder with experts in different domains Unresolved cited work

Reference 7

Resolution: malformed identifier; raw_fallback, observed 2026-05-25T00:05:07.884833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 10b1598a-f863-40bd-933d-3ed7229a700c · outbound

This paper cites We explore this method for acoustic mod- eling on two different tasks.

Teach an all-rounder with experts in different domains We explore this method for acoustic mod- eling on two different tasks

Reference 8

Resolution: malformed identifier; raw_fallback, observed 2026-05-25T00:05:07.888526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9206ef70-6136-48ef-a5ca-fd8f44b31643 · outbound

This paper cites Thus, we will explore this training strategy to improve the performance of LSTM mod- els in the future work.

Teach an all-rounder with experts in different domains Thus, we will explore this training strategy to improve the performance of LSTM mod- els in the future work

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.906576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d5bd335d-1190-46cd-b984-2c4cf3a2b8c1 · outbound

This paper cites Context- dependent pre-trained deep neural networks for large- vocabulary speech recognition.

Teach an all-rounder with experts in different domains Context- dependent pre-trained deep neural networks for large- vocabulary speech recognition

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.914567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d8bfe2e3-d53b-4004-95f1-ec8e96a582f8 · outbound

This paper cites Recent progresses in deep learning based acoustic models.

Teach an all-rounder with experts in different domains Recent progresses in deep learning based acoustic models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.867060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 119dd33e-95b4-4bf5-ae39-5476a63140ad · outbound

This paper cites A compara- tive analytic study on the gaussian mixture and context dependent deep neural network hidden markov models.

Teach an all-rounder with experts in different domains A compara- tive analytic study on the gaussian mixture and context dependent deep neural network hidden markov models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.876614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fd5783bc-6687-42d5-826e-21f782b488ea · outbound

This paper cites Speaker stress-resistant continuous speech recognition.

Teach an all-rounder with experts in different domains Speaker stress-resistant continuous speech recognition

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.801809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation df42d9c7-1d36-4b2b-aeb1-57622fdb753d · outbound

This paper cites Tandem con- nectionist feature extraction for conventional hmm sys- tems.

Teach an all-rounder with experts in different domains Tandem con- nectionist feature extraction for conventional hmm sys- tems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.807250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5a168023-13c9-4a76-b932-72f66ec5de99 · outbound

This paper cites An investigation of deep neural networks for noise robust speech recogni- tion.

Teach an all-rounder with experts in different domains An investigation of deep neural networks for noise robust speech recogni- tion

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.848131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4cdfa298-2a7c-446c-89df-a8226043ce23 · outbound

This paper cites Making machines understand us in reverberant rooms.

Teach an all-rounder with experts in different domains Making machines understand us in reverberant rooms

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.852392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 99bad173-9454-47d7-94e1-e5d9dfd0696c · outbound

This paper cites Speech enhance- ment with lstm recurrent neural networks and its ap- plication to noise-robust asr.

Teach an all-rounder with experts in different domains Speech enhance- ment with lstm recurrent neural networks and its ap- plication to noise-robust asr

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.862143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f3431438-a296-410d-b8ae-28a0d2a47c00 · outbound

This paper cites Domain adaptation using factorized hidden layer for robust automatic speech recognition.

Teach an all-rounder with experts in different domains Domain adaptation using factorized hidden layer for robust automatic speech recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.857147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6c76d451-1880-416b-a616-bb4dde903748 · outbound

This paper cites A study of enhancement, augmentation, and autoencoder methods for domain adaptation in distant speech recognition.

Teach an all-rounder with experts in different domains A study of enhancement, augmentation, and autoencoder methods for domain adaptation in distant speech recognition

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.834874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 45faf285-2521-444a-9538-d7df6a063edc · outbound

This paper cites Toward domain-invariant speech recognition via large scale training.

Teach an all-rounder with experts in different domains Toward domain-invariant speech recognition via large scale training

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-25T00:05:06.705655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 38f94c40-fc18-42ac-8892-01adf0945fe9 · outbound

This paper cites Do deep nets really need to be deep?.

Teach an all-rounder with experts in different domains Do deep nets really need to be deep?

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.842775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6875296d-589d-4807-89cb-0db8b0f33f5d · outbound

This paper cites Learning small-size dnn with output-distribution-based criteria.

Teach an all-rounder with experts in different domains Learning small-size dnn with output-distribution-based criteria

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.822380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 580abd6d-9c62-4025-a7c8-130faea5c5eb · outbound

This paper cites Distilling the knowledge in a neural network.

Teach an all-rounder with experts in different domains Distilling the knowledge in a neural network

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.826707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c5fca83d-0b85-4553-a1dc-4f3296122238 · outbound

This paper cites Distilling knowl- edge from ensembles of neural networks for speech recognition.

Teach an all-rounder with experts in different domains Distilling knowl- edge from ensembles of neural networks for speech recognition

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.795040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 29bb5449-2227-4046-939d-3874a9671e5a · outbound

This paper cites Learning from multiple teacher networks.

Teach an all-rounder with experts in different domains Learning from multiple teacher networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.831119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 48b5a618-1361-4d4a-abdd-ebbfdf7d6b62 · outbound

This paper cites Syllable-based acoustic modeling with ctc-smbr-lstm.

Teach an all-rounder with experts in different domains Syllable-based acoustic modeling with ctc-smbr-lstm

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.839027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation abdfc24f-f1ed-4229-ae0a-1f50dba6bcd9 · outbound

This paper cites Image method for efﬁ- ciently simulating small-room acoustics.

Teach an all-rounder with experts in different domains Image method for efﬁ- ciently simulating small-room acoustics

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.871746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ae9296b4-ee24-4fcf-9e91-9b4f37a66ef5 · outbound

This paper cites Learning feature mapping using deep neu- ral network bottleneck features for distant large vocab- ulary speech recognition.

Teach an all-rounder with experts in different domains Learning feature mapping using deep neu- ral network bottleneck features for distant large vocab- ulary speech recognition

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.918479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7b6655f5-2641-43e8-a361-c005abd980f4 · outbound

This paper cites Deep-FSMN for Large Vocabulary Continuous Speech Recognition.

Teach an all-rounder with experts in different domains Deep-FSMN for Large Vocabulary Continuous Speech Recognition

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-25T00:05:06.712218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e1432a59-2ce7-4c8d-a005-a6370112ccf8 · outbound

This paper cites The kaldi speech recognition toolkit.

Teach an all-rounder with experts in different domains The kaldi speech recognition toolkit

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.811542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6f358f72-65b3-420b-8a2a-533ed430cbfc · outbound

This paper cites Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update ﬁlter- ing.

Teach an all-rounder with experts in different domains Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update ﬁlter- ing

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T00:05:07.817295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation d625403a-032d-4c2b-a6a0-f6880c574f3a · inbound