Paper Citation Record · LEDGER

Neural networks for Text-to-Speech evaluation

As of 21 July 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2604.08562.

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

pith.paper-citation-record.v1
2604.08562 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-15T09:46:26.884551Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-21T06:31:05.380196+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

11 of 11 outbound references displayed

verified exact3
verified fuzzy7
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 61c536a8-0ba4-4236-be5b-0e981f3f4dc5 · outbound

This paper cites UTMOS: UTokyo-SaruLab system for V oiceMOS challenge 2022.

Neural networks for Text-to-Speech evaluation UTMOS: UTokyo-SaruLab system for V oiceMOS challenge 2022

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T09:49:55.743688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 673e9db9-e926-47f2-b071-2542a5f82e3f · outbound

This paper cites V oiceMOS challenge 2024.

Neural networks for Text-to-Speech evaluation V oiceMOS challenge 2024

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T09:49:55.734529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation f7728530-8560-4a6b-9fcb-4bc70517722d · outbound

This paper cites HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units.

Neural networks for Text-to-Speech evaluation HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T09:49:54.655165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 10b34981-1018-4791-b2c0-c0e76fd61647 · outbound

This paper cites MOSNet: Deep Learning based Objective Assessment for Voice Conversion.

Neural networks for Text-to-Speech evaluation MOSNet: Deep Learning based Objective Assessment for Voice Conversion

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:49:54.671517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation dbdc8cc8-b4f2-450e-ab10-af09588d565d · outbound

This paper cites SOMOS: The samsung open MOS dataset for the prediction of synthesized speech quality.

Neural networks for Text-to-Speech evaluation SOMOS: The samsung open MOS dataset for the prediction of synthesized speech quality

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T09:49:55.746463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 4f468470-51c7-440e-954a-a496d2c6f175 · outbound

This paper cites DNSMOS: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors.

Neural networks for Text-to-Speech evaluation DNSMOS: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T09:49:55.732480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 91b98aed-305e-48c0-a438-6ca2ca1cf77b · outbound

This paper cites DistilMOS: Layer-wise self-distillation for self-supervised learning model-based MOS prediction.

Neural networks for Text-to-Speech evaluation DistilMOS: Layer-wise self-distillation for self-supervised learning model-based MOS prediction

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:49:54.666254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 5ab1aab4-083e-418d-bb73-46b9af2e6031 · outbound

This paper cites NISQA: A deep CNN-self-attention model for multidimensional speech quality prediction.

Neural networks for Text-to-Speech evaluation NISQA: A deep CNN-self-attention model for multidimensional speech quality prediction

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T09:49:55.738869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation b4fc9838-1ee2-45d2-af4f-b4ab34e01edd · outbound

This paper cites Neural side-by-side: Predicting human preferences for no-reference super- resolution evaluation.

Neural networks for Text-to-Speech evaluation Neural side-by-side: Predicting human preferences for no-reference super- resolution evaluation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T09:49:55.741573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 7ae38dbf-31a7-4f26-8b39-9b76eaf5a811 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Neural networks for Text-to-Speech evaluation The unreasonable effectiveness of deep features as a perceptual metric

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T09:49:55.736894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 00bb5a82-a27e-4b68-bd2b-675f021571e6 · outbound

This paper cites SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities.

Neural networks for Text-to-Speech evaluation SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:49:54.660606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Pith citing papers

No inbound Pith citation observations are available.