Paper Citation Record · LEDGER

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR

As of 13 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2509.08173.

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

pith.paper-citation-record.v1
2509.08173 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:14:15.382375Z

measured 49 of 49 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:14:12.433222Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-04T21:14:15.628368Z

Reference resolution

48 of 48 outbound references displayed

verified exact2
verified fuzzy41
unresolved5
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e5c0ae98-f2d8-4c03-8a93-3b59e89286ad · outbound

This paper cites an unresolved cited work.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-04T21:14:18.274398Z

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.

Observation e9dfd7fa-8bc1-456a-9c70-599f69ef689e · outbound

This paper cites A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-04T21:14:15.681500Z

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.

Observation e049eea2-f200-40c5-ad8f-56946dd76739 · outbound

This paper cites Attribute Recognition and Knowledge Integration The proposed bottom-up framework is illustrated in Figure 1.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Attribute Recognition and Knowledge Integration The proposed bottom-up framework is illustrated in Figure 1

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.246637Z

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.

Observation 97795ed0-5a65-4ff4-a32f-b9cfeb12a9ab · outbound

This paper cites an unresolved cited work.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-04T21:14:18.260725Z

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.

Observation 6ba4e3bc-6e83-4dd9-9b97-ee80a824301f · outbound

This paper cites basic5000.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR basic5000

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.232151Z

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.

Observation 30dae91a-7cb7-4d3f-a6a2-7c6afb97bfa0 · outbound

This paper cites Speech recognition by machines and humans,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Speech recognition by machines and humans,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.117690Z

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.

Observation 47765219-fd24-475b-8517-7be0568c4b43 · outbound

This paper cites an unresolved cited work.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-04T21:14:18.202976Z

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.

Observation ebf116fd-b126-401a-917d-3da8161ac9a8 · outbound

This paper cites Continuous speech recognition by statistical methods,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Continuous speech recognition by statistical methods,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.187774Z

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

Observation f8ca8fbf-c283-4815-b70e-10f240c2bbc5 · outbound

This paper cites The kaldi speech recognition toolkit,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR The kaldi speech recognition toolkit,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.174279Z

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.

Observation 4bc0ba0a-f0ae-4287-9dde-a4c2b6730c12 · outbound

This paper cites Large-vocabulary speaker-independent con- tinuous speech recognition using hmm,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Large-vocabulary speaker-independent con- tinuous speech recognition using hmm,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.159896Z

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.

Observation 5c358c93-1c48-4aa4-af43-f26eb0cccaed · outbound

This paper cites An information- extraction approach to speech processing: Analysis, detection, verifi- cation, and recognition,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR An information- extraction approach to speech processing: Analysis, detection, verifi- cation, and recognition,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.146102Z

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.

Observation b55f293d-050d-4467-a9b0-58622b132628 · outbound

This paper cites Allen,How do Humans Process and Recognize Speech?, Springer US, Boston, MA, 1995.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Allen,How do Humans Process and Recognize Speech?, Springer US, Boston, MA, 1995

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.131682Z

Source-reported events for the cited work

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Observation e4e042fc-f1cd-48b8-b9c6-eb62218f1dfc · outbound

This paper cites Combining articulatory and acoustic information for speech recognition in noisy and reverberant environments,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Combining articulatory and acoustic information for speech recognition in noisy and reverberant environments,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.019722Z

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.

Observation bfa46a26-a5be-474c-83ad-0846300d7cdb · outbound

This paper cites an unresolved cited work.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-04T21:14:18.103873Z

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.

Observation 29117e98-c383-4fd4-8433-520b5278ec99 · outbound

This paper cites The geometry of phonological features,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR The geometry of phonological features,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.089009Z

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.

Observation 56a7eb3c-706f-487b-b58c-b62c9d73840d · outbound

This paper cites Hybrid ctc- attention based end-to-end speech recognition using subword units,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Hybrid ctc- attention based end-to-end speech recognition using subword units,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.075152Z

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.

Observation c73eca58-d0d8-4732-9914-78059185e28e · outbound

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

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR ESPnet: End-to-end speech processing toolkit,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.061216Z

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.

Observation 7ae045aa-56f6-43e0-a58f-3a964f59d719 · outbound

This paper cites Hybrid ctc/attention architecture for end-to-end speech recognition,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Hybrid ctc/attention architecture for end-to-end speech recognition,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.047846Z

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.

Observation e192ca46-598f-4591-b27f-f96af3b9507c · outbound

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

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Robust speech recognition via large- scale weak supervision,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.033479Z

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.

Observation ed184326-b178-4ef7-ae5d-5de0a2c913a6 · outbound

This paper cites Syllable-based large vocabulary continuous speech recogni- tion,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Syllable-based large vocabulary continuous speech recogni- tion,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.928092Z

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.

Observation 1289e771-f6fd-4a4c-b959-3bf68b0e0f09 · outbound

This paper cites dissertation, Carnegie Mellon University, Pitts- burgh, PA, USA, 1992.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR dissertation, Carnegie Mellon University, Pitts- burgh, PA, USA, 1992

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.006192Z

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.

Observation a9c17f3c-fafa-472b-9c1b-034a57494ad2 · outbound

This paper cites Language-universal speech attributes modeling for zero-shot multilin- gual spoken keyword recognition,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Language-universal speech attributes modeling for zero-shot multilin- gual spoken keyword recognition,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.992674Z

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.

Observation 80bb1f80-2166-41a3-a713-4fa64b968a37 · outbound

This paper cites Detection-based asr in the au- tomatic speech attribute transcription project,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Detection-based asr in the au- tomatic speech attribute transcription project,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.980325Z

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.

Observation 03989220-93b6-42bd-9283-c29283cbf881 · outbound

This paper cites A flexible stream architecture for asr using articulatory features,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR A flexible stream architecture for asr using articulatory features,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.967045Z

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.

Observation ba7c39c6-494c-4924-b674-65c54e432489 · outbound

This paper cites dissertation, Massachusetts Institute of Tech- nology, Cambridge, MA, USA, 1996.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR dissertation, Massachusetts Institute of Tech- nology, Cambridge, MA, USA, 1996

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.954888Z

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.

Observation 25f8fb4b-f3ea-4c2f-855b-efc69f210710 · outbound

This paper cites An event-based acoustic-phonetic approach to speech segmentation and e-set recogni- tion,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR An event-based acoustic-phonetic approach to speech segmentation and e-set recogni- tion,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.941509Z

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.

Observation b79c6b63-1f57-4a5e-8f52-b2e390cc7f09 · outbound

This paper cites What makes a word: Learning base units in Japanese for speech recognition,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR What makes a word: Learning base units in Japanese for speech recognition,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.849985Z

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.

Observation 03858724-e8d2-47ea-939f-df3d8f1cf2d8 · outbound

This paper cites Context-dependent syllable acoustic model for continuous chinese speech recognition,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Context-dependent syllable acoustic model for continuous chinese speech recognition,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.914990Z

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.

Observation d2de5e83-a667-4a55-8962-a73f53603e64 · outbound

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

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Syllable-based acoustic modeling with ctc-smbr-lstm,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.901323Z

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.

Observation d5971335-de77-49c2-8466-6ce6bef2c373 · outbound

This paper cites A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-04T21:14:15.533771Z

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.

Observation 3a8637f4-e988-49fe-9d85-23cd830e3937 · outbound

This paper cites Syllable-based sequence-to-sequence speech recognition with the transformer in man- darin chinese,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Syllable-based sequence-to-sequence speech recognition with the transformer in man- darin chinese,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.888229Z

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.

Observation b003f7be-d80b-4b59-a61b-65f8a20c3510 · outbound

This paper cites Decoupling recognition and transcription in mandarin asr,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Decoupling recognition and transcription in mandarin asr,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.875150Z

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.

Observation e5d5b57d-1451-4dfc-b6ea-489b6153fbfb · outbound

This paper cites The mora and syllable structure in japanese: Evi- dence from speech errors,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR The mora and syllable structure in japanese: Evi- dence from speech errors,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.862796Z

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.

Observation a00cee17-f750-4f6a-9854-f9a92e33986c · outbound

This paper cites Akamatsu,Japanese phonetics : theory and practice / Tsu- tomu Akamatsu, LINCOM studies in Asian linguistics ; 3.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Akamatsu,Japanese phonetics : theory and practice / Tsu- tomu Akamatsu, LINCOM studies in Asian linguistics ; 3

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:16.997440Z

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.

Observation b94b1492-422d-4d0b-9d5c-a04168fb3cb0 · outbound

This paper cites Syllable recognition us- ing syllable-segment statistics and syllable-based hmm,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Syllable recognition us- ing syllable-segment statistics and syllable-based hmm,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.837041Z

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.

Observation 3b7f773f-61ee-45f7-aeb1-ba0bde1fa039 · outbound

This paper cites Compari- son of syllable-based and phoneme-based dnn-hmm in japanese speech recognition,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Compari- son of syllable-based and phoneme-based dnn-hmm in japanese speech recognition,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.823654Z

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.

Observation 1afdcdb8-812d-4f26-bdfe-e364eed41ed1 · outbound

This paper cites Wavlm: Large-scale self-supervised pre-training for full stack speech processing,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Wavlm: Large-scale self-supervised pre-training for full stack speech processing,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.810621Z

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.

Observation 1fc5285c-e264-495b-a59b-e392712be29e · outbound

This paper cites Fant,Speech Sounds and Features, The MIT Press, 1973.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Fant,Speech Sounds and Features, The MIT Press, 1973

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.793815Z

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.

Observation d5a7fc4f-89ce-4c69-8d92-2fc91e91ecdd · outbound

This paper cites Ladefoged and S.F.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Ladefoged and S.F

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.683076Z

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.

Observation ae59aec8-fcb1-4bc5-928a-d9a6377236d7 · outbound

This paper cites To support decoding with CTC model, we trained separate KenLM language models tailored to each modeling unit.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR To support decoding with CTC model, we trained separate KenLM language models tailored to each modeling unit

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:18.218047Z

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.

Observation dfb80fda-59ee-4ce8-a97c-e7d1722a2893 · outbound

This paper cites Modeling linguistic fea- tures in speech recognition,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Modeling linguistic fea- tures in speech recognition,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:17.463595Z

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.

Observation eb0edb80-68e6-441f-b3c1-d970e386b300 · outbound

This paper cites Acoustic cues of the stop voicing contrast in mod- ern tokyo japanese,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Acoustic cues of the stop voicing contrast in mod- ern tokyo japanese,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:16.652414Z

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.

Observation a970f853-17bc-488b-914b-ac9f865889b1 · outbound

This paper cites Syllable-based acoustic modeling for japanese spontaneous speech recognition,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Syllable-based acoustic modeling for japanese spontaneous speech recognition,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:16.476687Z

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.

Observation 2329b416-c6e2-4b47-a9ba-7663f0922ff4 · outbound

This paper cites Aishell- 1: An open-source mandarin speech corpus and a speech recognition baseline,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Aishell- 1: An open-source mandarin speech corpus and a speech recognition baseline,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:16.289241Z

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.

Observation 4554dadd-6888-4231-b273-01e35ad8b690 · outbound

This paper cites JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T21:14:15.181943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff822c4a-456e-4a59-aa1e-eb8869cec52c · outbound

This paper cites Atten- tion is all you need,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Atten- tion is all you need,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:16.152917Z

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.

Observation 8661e1ac-fd37-4922-ab76-4868086bd10d · outbound

This paper cites Decoupled weight decay regular- ization,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Decoupled weight decay regular- ization,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:16.013203Z

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.

Observation 16060b58-cf3e-42c6-ab77-49e6808e6d6c · outbound

This paper cites Mls: A large-scale multilingual dataset for speech research,.

A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR Mls: A large-scale multilingual dataset for speech research,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:14:15.825217Z

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.

Pith citing papers

Observation e9dfd7fa-8bc1-456a-9c70-599f69ef689e · inbound