Paper Citation Record · LEDGER

ML-SUPERB: Multilingual Speech Universal PERformance Benchmark

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2305.10615.

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

pith.paper-citation-record.v1
2305.10615 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:48:43.471055Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a2aa102a-7ddc-4a8a-bed5-580eabcf537c · inbound

ParaLBench: A Large-Scale Benchmark for Computational Paralinguistics over Acoustic Foundation Models cites this paper.

ParaLBench: A Large-Scale Benchmark for Computational Paralinguistics over Acoustic Foundation Models ML-SUPERB: Multilingual Speech Universal PERformance Benchmark

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T20:48:43.471055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8976bf4a-5cc0-44ad-ba51-f47f190fc29d · inbound

CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing cites this paper.

CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing ML-SUPERB: Multilingual Speech Universal PERformance Benchmark

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T21:30:10.947236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7da4468-3076-44dc-a9eb-cc06b1140fcc · inbound

DuRep: Dual-Mode Speech Representation Learning via ASR-Aware Distillation cites this paper.

DuRep: Dual-Mode Speech Representation Learning via ASR-Aware Distillation ML-SUPERB: Multilingual Speech Universal PERformance Benchmark

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:12:49.072445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6fc5be0-002e-46d2-8fd1-8bf1bf890be5 · inbound

A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection cites this paper.

A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection ML-SUPERB: Multilingual Speech Universal PERformance Benchmark

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-15T17:26:22.145214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4d157ba1-bee3-4ac8-89f9-c1b010028d89 · inbound

BlasBench: An Open Benchmark for Irish Speech Recognition cites this paper.

BlasBench: An Open Benchmark for Irish Speech Recognition ML-SUPERB: Multilingual Speech Universal PERformance Benchmark

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:41:01.636836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation da993b4c-019f-4f4c-97f0-83e6676f313e · inbound

A framework for analyzing concept representations in neural models cites this paper.

A framework for analyzing concept representations in neural models ML-SUPERB: Multilingual Speech Universal PERformance Benchmark

Reference 205

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T22:18:59.044436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 37093dc2-ae14-4c77-a490-d95b3faaa6fa · inbound

Multi-layer attentive probing improves transfer of audio representations for bioacoustics cites this paper.

Multi-layer attentive probing improves transfer of audio representations for bioacoustics ML-SUPERB: Multilingual Speech Universal PERformance Benchmark

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:31:28.371659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.