Paper Citation Record · LEDGER

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems

As of 20 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2411.08375.

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

pith.paper-citation-record.v1
2411.08375 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:44:06.892104Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

34 of 34 outbound references displayed

verified exact8
verified fuzzy18
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6242df02-9c0f-41ff-b406-f3ce7e7fae00 · outbound

This paper cites Some Experiments on the Recognition of Speech, with One and with Two Ears,,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Some Experiments on the Recognition of Speech, with One and with Two Ears,,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.550867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b0b47b5e-7155-448d-bb44-8cecbf942ae9 · outbound

This paper cites DEEP CLUSTERING:DISCRIMINATIVE EMBEDDINGS FOR SEGMENTATION AND SEPARATION,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems DEEP CLUSTERING:DISCRIMINATIVE EMBEDDINGS FOR SEGMENTATION AND SEPARATION,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.535543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b8fc8414-9043-4d70-bb93-8bc039d114ca · outbound

This paper cites Deep attractor network for single -microphone speaker separation,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Deep attractor network for single -microphone speaker separation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.519744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f7dbf720-2f5e-417a-bd2b-b4607360eb65 · outbound

This paper cites Attention is All You Need in Speech Separation.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Attention is All You Need in Speech Separation

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:44:07.263187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 52a5dc20-cccf-470c-b98f-8c83a193c7c0 · outbound

This paper cites Improved Speech Separation with Time-and-Frequency Cross-domain Joint Embedding and Clustering.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Improved Speech Separation with Time-and-Frequency Cross-domain Joint Embedding and Clustering

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:44:07.240866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 12da9083-97c1-4a26-a103-7b8dcbb5051f · outbound

This paper cites Permutation invariant tr aining of deep models for speaker-independent multi-talker speech separation,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Permutation invariant tr aining of deep models for speaker-independent multi-talker speech separation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.505914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 368cfe2b-511d-4a01-b64a-3ef0f79892d8 · outbound

This paper cites Multitalker speech separation with utterance -level permutation invariant training of deep recurrent neural networks,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Multitalker speech separation with utterance -level permutation invariant training of deep recurrent neural networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.491009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d36ac75d-b6a5-411f-87bd-cf8c36d2079b · outbound

This paper cites TF-GridNet: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems TF-GridNet: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:44:07.217872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 554c99af-b296-434d-90a8-aa4e4310e4a0 · outbound

This paper cites TASNET: TIME -DOMAIN AUDIO SEPARATION NETWORK FOR REAL-TIME, SINGLE-CHANNEL SPEECH SEPARATION,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems TASNET: TIME -DOMAIN AUDIO SEPARATION NETWORK FOR REAL-TIME, SINGLE-CHANNEL SPEECH SEPARATION,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.475317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3a6fdef7-e845-47e9-a23f-1a05acbcc285 · outbound

This paper cites Conv -TasNet: Surpassing Ideal Time –Frequency Magnitude Masking f or Speech Separation,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Conv -TasNet: Surpassing Ideal Time –Frequency Magnitude Masking f or Speech Separation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.459310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7e727076-d0d4-44f1-b626-7727c192a461 · outbound

This paper cites DUAL -PATH RNN: EFFICIENT LONG SEQUENCE MODELING FOR TIME -DOMAIN SINGLE -CHANNEL SPEECH SEPARATION,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems DUAL -PATH RNN: EFFICIENT LONG SEQUENCE MODELING FOR TIME -DOMAIN SINGLE -CHANNEL SPEECH SEPARATION,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.443573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4f1e8e8d-6499-4d75-af75-dfb71656216b · outbound

This paper cites Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T21:44:06.786141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 787d9136-6901-4951-8f9d-39467df0b265 · outbound

This paper cites Wavesplit: End -to-End Speech Separation by Speaker Clustering,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Wavesplit: End -to-End Speech Separation by Speaker Clustering,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.427437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation da96ce6e-3e96-4322-880e-bab598c22e02 · outbound

This paper cites Tiny-Sepformer: A Tiny Time-Domain Transformer Network for Speech Separation.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Tiny-Sepformer: A Tiny Time-Domain Transformer Network for Speech Separation

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:44:07.162751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 358a27d7-a61c-4c7b-8d47-2e9047218667 · outbound

This paper cites Divide and Conquer: A Deep CASA Approach to Talker -independent Monaural Speaker Separation,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Divide and Conquer: A Deep CASA Approach to Talker -independent Monaural Speaker Separation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.413011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 99720451-ec14-4abe-a7cf-b74f03007741 · outbound

This paper cites REAL-M: Towards Speech Separation on Real Mixtures.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems REAL-M: Towards Speech Separation on Real Mixtures

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:44:07.137918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e26d453e-01c8-4976-b519-5dc8589d1e44 · outbound

This paper cites WHAM!: Extending Speech Separation to Noisy Environments.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems WHAM!: Extending Speech Separation to Noisy Environments

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T21:44:06.811233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 762c9a37-0bd2-4e88-9802-1f6ea8e1ac4d · outbound

This paper cites WHAMR!: NOISY AND REVERBERANT SINGLE -CHANNEL SPEECH SEPARATION,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems WHAMR!: NOISY AND REVERBERANT SINGLE -CHANNEL SPEECH SEPARATION,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.397031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 22292930-4f3e-4ae9-bc5d-9aee646b3d76 · outbound

This paper cites LibriMix: An Open-Source Dataset for Generalizable Speech Separation.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems LibriMix: An Open-Source Dataset for Generalizable Speech Separation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T21:44:06.821525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65fe14a5-da27-4bc9-828e-fc6db37f10d5 · outbound

This paper cites THE THIRD ‘CHIME’ SPEECH SEPARATION AND RECOGNITION CHALLENGE: DATASET, TASK AND BASELINES,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems THE THIRD ‘CHIME’ SPEECH SEPARATION AND RECOGNITION CHALLENGE: DATASET, TASK AND BASELINES,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.380207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 39ed1235-5b17-4a12-8b08-d0c46a168cd1 · outbound

This paper cites The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T21:44:06.831042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f101fef0-1c28-4e77-88ba-0d97bf7e41f8 · outbound

This paper cites The Mixer 6 Corpus:Resources for Cross -Channel and Text Independent Speaker Recognition,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems The Mixer 6 Corpus:Resources for Cross -Channel and Text Independent Speaker Recognition,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.364078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b45346b4-f5b5-4cb9-853a-5ad664b904c6 · outbound

This paper cites VoxCeleb: a large-scale speaker identification dataset.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems VoxCeleb: a large-scale speaker identification dataset

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T21:44:06.840985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca76fa13-58aa-4c01-a124-b2fcddd97c49 · outbound

This paper cites Training Noisy Single-Channel Speech Separation With Noisy Oracle Sources: A Large Gap and A Small Step.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Training Noisy Single-Channel Speech Separation With Noisy Oracle Sources: A Large Gap and A Small Step

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:44:07.031434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ebdeb696-e390-401c-84b9-61990449cf26 · outbound

This paper cites A Gender Mixture Detection Approach to Unsupervised Single-Channel Speech Separation Based on Deep Neural Networks,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems A Gender Mixture Detection Approach to Unsupervised Single-Channel Speech Separation Based on Deep Neural Networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.347951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b67f0ced-2a61-4b83-9942-eda846691c5f · outbound

This paper cites Remix-cycle-consistent Learning on Adversarially Learned Separator for Accurate and Stable Unsupervised Speech Separation.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Remix-cycle-consistent Learning on Adversarially Learned Separator for Accurate and Stable Unsupervised Speech Separation

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:44:07.009425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 599dbe93-b34b-4fbc-bbfe-54952f2b6288 · outbound

This paper cites Unsupervised Sound Separation Using Mixture Invariant Training.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Unsupervised Sound Separation Using Mixture Invariant Training

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T21:44:06.858284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97d61a57-e446-4260-a215-21611950a792 · outbound

This paper cites Teacher-Student MixIT for Unsupervised and Semi-supervised Speech Separation.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Teacher-Student MixIT for Unsupervised and Semi-supervised Speech Separation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T21:44:06.862971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb4f0c0b-bb5e-4876-97ab-33e503801030 · outbound

This paper cites Heterogeneous separation consistency training for adaptation of unsupervised speech separation,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Heterogeneous separation consistency training for adaptation of unsupervised speech separation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.331929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 26b0a7c3-c563-4c42-82bf-bea629696620 · outbound

This paper cites DARPA TIMIT acoustic -phonetic continous speech corpus CD -ROM. NIST speech disc 1 -1.1,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems DARPA TIMIT acoustic -phonetic continous speech corpus CD -ROM. NIST speech disc 1 -1.1,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.316284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 63a1e8cf-4290-45a2-8763-14d57ec2b152 · outbound

This paper cites Building Corpora for Single-Channel Speech Separation Across Multiple Domains.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Building Corpora for Single-Channel Speech Separation Across Multiple Domains

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:44:06.951143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e14ac124-b950-401a-acd8-0774e5195f99 · outbound

This paper cites Improving deep attractor network by BGRU and GMM for speech separation,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Improving deep attractor network by BGRU and GMM for speech separation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.299489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7a13a1ab-50ed-47b8-9406-5b3f94335c0e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems Adam: A Method for Stochastic Optimization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T21:44:06.887108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38b53c93-5111-43de-8bb0-01d5effa76ed · outbound

This paper cites SDR – HALF-BAKED OR WELL DONE?,.

Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems SDR – HALF-BAKED OR WELL DONE?,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:44:07.280722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.