Paper Citation Record · LEDGER

Very Deep Self-Attention Networks for End-to-End Speech Recognition

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1904.13377.

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

pith.paper-citation-record.v1
1904.13377 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:37:54.757887Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-01T10:05:41.160122Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4dd5d653-295b-4f15-b764-147929974035 · inbound

Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention cites this paper.

Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention Very Deep Self-Attention Networks for End-to-End Speech Recognition

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T10:23:02.157631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ff11374-f745-4777-9b3b-f29fd2a75b74 · inbound

From Silent Signals to Natural Language: A Dual-Stage Transformer-LLM Approach cites this paper.

From Silent Signals to Natural Language: A Dual-Stage Transformer-LLM Approach Very Deep Self-Attention Networks for End-to-End Speech Recognition

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T11:39:56.523465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c1a6edb-f924-4127-8618-9872a7f56a7d · inbound

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study cites this paper.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study Very Deep Self-Attention Networks for End-to-End Speech Recognition

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-01T10:05:41.161352Z

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 f83f31c8-b898-41b9-9946-fb80c9d7f038 · inbound

How to Recognize New Words: A Comparison Between Context Biasing Methods and Speech LLMs cites this paper.

How to Recognize New Words: A Comparison Between Context Biasing Methods and Speech LLMs Very Deep Self-Attention Networks for End-to-End Speech Recognition

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T14:37:54.757887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.