Paper Citation Record · LEDGER

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation

As of 13 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2412.08306.

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

pith.paper-citation-record.v1
2412.08306 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:01:45.543391Z

measured 36 of 36 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

verified exact3
verified fuzzy24
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c813cf80-5030-4cf8-b33a-17e665f6b1dd · outbound

This paper cites an unresolved cited work.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-11T18:01:45.976320Z

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 520e0775-02d9-4757-8a08-a363f24b8d6a · outbound

This paper cites Classification of lexical stress using spectral and prosodic features for computer-assisted language learning systems,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Classification of lexical stress using spectral and prosodic features for computer-assisted language learning systems,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.965399Z

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 5838e5b4-57d8-4654-9184-55d89cf6f322 · outbound

This paper cites an unresolved cited work.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-11T18:01:45.952398Z

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 bdafc196-15c6-4c8d-9d86-5b7ccd88cb2b · outbound

This paper cites Speech enhancement with lstm recurrent neural networks and its application to noise-robust ASR,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Speech enhancement with lstm recurrent neural networks and its application to noise-robust ASR,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.941421Z

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 30fda546-516a-4bab-971a-1ffc858944ad · outbound

This paper cites Towards robust speech emotion recognition using deep residual networks for speech enhancement,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Towards robust speech emotion recognition using deep residual networks for speech enhancement,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.930533Z

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 7fb9d207-4105-4ab2-922e-52cad6f52065 · outbound

This paper cites Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-11T18:01:45.652571Z

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 022b7fa5-8220-4a72-96c4-710de65fea3c · outbound

This paper cites SLIM prosodic module for learning activities in a foreign language,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation SLIM prosodic module for learning activities in a foreign language,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.920479Z

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 e57530f9-a75f-4e53-8a6a-64b3d3c68ac7 · outbound

This paper cites Automatic syllable stress detection using prosodic features for pronunciation evaluation of language learners,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Automatic syllable stress detection using prosodic features for pronunciation evaluation of language learners,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.910123Z

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 465432fe-dd0f-457f-a66b-4050c920bc14 · outbound

This paper cites Automatic prominent syllable detection with machine learning classifiers,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Automatic prominent syllable detection with machine learning classifiers,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.898406Z

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 1a9be29a-0a2d-4602-8fbf-691ca703a660 · outbound

This paper cites An Attention Based Deep Neural Network for Automatic Lexical Stress Detection,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation An Attention Based Deep Neural Network for Automatic Lexical Stress Detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.886399Z

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 fd9fb64a-6182-40a7-9450-16c228d21fba · outbound

This paper cites An End-to-end Approach for Lexical Stress Detection based on Transformer.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation An End-to-end Approach for Lexical Stress Detection based on Transformer

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-11T18:01:45.636790Z

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 e59973ce-6058-4040-8648-b8b7c3cec32f · outbound

This paper cites A compar- ison of learned representations with jointly optimized vae and dnn for syllable stress detection,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation A compar- ison of learned representations with jointly optimized vae and dnn for syllable stress detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.874565Z

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 b03fb486-12b9-4f5a-8c8b-e36712302239 · outbound

This paper cites The ISLE corpus of non-native spoken English,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation The ISLE corpus of non-native spoken English,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.863238Z

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 b489738f-4b14-4d3a-82cc-86c997ab90f6 · outbound

This paper cites Automatic detection of syllable stress using sonority based prominence features for pronunciation evaluation,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Automatic detection of syllable stress using sonority based prominence features for pronunciation evaluation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.849671Z

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 e5a30a82-ab85-43ce-8847-58633a92be9f · outbound

This paper cites Comparison of automatic syllable stress detection quality with time-aligned boundaries and context dependencies,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Comparison of automatic syllable stress detection quality with time-aligned boundaries and context dependencies,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.838997Z

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 87613730-d866-43d1-a29f-8abdd4115c5b · outbound

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

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T18:01:45.473029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0413e040-8369-4c82-adb9-9fdbe37e98ae · outbound

This paper cites Hubert: Self- supervised speech representation learning by masked prediction of hidden units,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Hubert: Self- supervised speech representation learning by masked prediction of hidden units,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.819980Z

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 f0b56a61-8a58-462f-9b29-4da926c1f1ca · outbound

This paper cites Multi-lingual multi-task speech emotion recognition using wav2vec 2.0,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Multi-lingual multi-task speech emotion recognition using wav2vec 2.0,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.804844Z

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 f1e94387-1dda-4d75-bd24-99ceceb379fd · outbound

This paper cites A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T18:01:45.483468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86868026-b868-4162-b636-c407cef57099 · outbound

This paper cites Exploring the use of self-supervised representations for automatic syllable stress detection,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Exploring the use of self-supervised representations for automatic syllable stress detection,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.793208Z

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 900ed1ea-dbdc-41b6-a73d-f730ab010ee8 · outbound

This paper cites Conv-tasnet: Surpassing ideal time– frequency magnitude masking for speech separation,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Conv-tasnet: Surpassing ideal time– frequency magnitude masking for speech separation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.781591Z

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 ca39b5ce-0a08-4823-96e6-68e02518e66c · outbound

This paper cites Real-time single-channel dereverbera- tion and separation with time-domain audio separation network.,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Real-time single-channel dereverbera- tion and separation with time-domain audio separation network.,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.769382Z

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 140c2dba-9105-4fb9-ae09-ebcde707dc79 · outbound

This paper cites U-net: Con- volutional networks for biomedical image segmentation,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation U-net: Con- volutional networks for biomedical image segmentation,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T18:01:45.495944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38ba5986-d49f-4269-8ebb-8773891998eb · outbound

This paper cites Tasnet: time-domain audio separation network for real-time, single-channel speech separation,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Tasnet: time-domain audio separation network for real-time, single-channel speech separation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.751948Z

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 d25ae8c9-6e10-44c6-9cbe-acd57126371f · outbound

This paper cites Conditional diffusion probabilistic model for speech enhancement,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Conditional diffusion probabilistic model for speech enhancement,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.740413Z

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 6febedc3-1816-4266-bece-725c599bea8a · outbound

This paper cites Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-11T18:01:45.612305Z

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 d2a9969f-bc3d-448d-9e7f-2207893b59f5 · outbound

This paper cites Librispeech: an asr corpus based on public domain audio books,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Librispeech: an asr corpus based on public domain audio books,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T18:01:45.509317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09cecc1d-516c-446f-92ee-3ff860578fd8 · outbound

This paper cites Audio set: An ontology and human-labeled dataset for audio events,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Audio set: An ontology and human-labeled dataset for audio events,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.722836Z

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 5bd1b9eb-ab91-4d6d-a71a-e2956e6a4ef6 · outbound

This paper cites The di- verse environments multi-channel acoustic noise database (demand): A database of multichannel environmental noise recordings,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation The di- verse environments multi-channel acoustic noise database (demand): A database of multichannel environmental noise recordings,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.710802Z

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 680f913d-f745-4c46-a3ed-eade7fd07406 · outbound

This paper cites Music Source Separation in the Waveform Domain.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Music Source Separation in the Waveform Domain

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T18:01:45.520297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d18656fa-b7f5-4f06-bcfe-490d8ac71dd2 · outbound

This paper cites Real Time Speech Enhancement in the Waveform Domain.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Real Time Speech Enhancement in the Waveform Domain

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T18:01:45.524228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ea2faf2-a87a-4911-aebc-82cabbe7a6b1 · outbound

This paper cites Noisy speech database for training speech enhancement algorithms and tts models,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Noisy speech database for training speech enhancement algorithms and tts models,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.700386Z

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 7ae9bdcf-6bbe-42cc-a5ff-f6e3f95f22b6 · outbound

This paper cites The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T18:01:45.532474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f48d5d6-701e-4a65-9dd8-8c6a5abfc47b · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermody- namics,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Deep unsupervised learning using nonequilibrium thermody- namics,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.688509Z

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 cb191950-a0fa-445e-a677-ae815ded3e1d · outbound

This paper cites Investigating RNN-based speech enhancement methods for noise-robust text-to-speech.,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation Investigating RNN-based speech enhancement methods for noise-robust text-to-speech.,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.677767Z

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 93cc4da0-aa29-45b0-b7b5-9021059e34da · outbound

This paper cites The voice bank corpus: Design, collection and data analysis of a large regional accent speech database,.

Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation The voice bank corpus: Design, collection and data analysis of a large regional accent speech database,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:01:45.666030Z

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

No inbound Pith citation observations are available.