Paper Citation Record · LEDGER

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation

As of 11 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2501.02512.

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

pith.paper-citation-record.v1
2501.02512 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:13:12.444746Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

29 of 29 outbound references displayed

verified exact0
verified fuzzy25
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fa1ee605-4d01-4c8b-9f59-7b9cd1c253a9 · outbound

This paper cites Major depressive disorder,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Major depressive disorder,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:14.927553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ab5e073a-eb8b-44c5-b122-5c36ab601bdb · outbound

This paper cites Insights into detecting adult ADHD symptoms through advanced dual-stream machine learning,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Insights into detecting adult ADHD symptoms through advanced dual-stream machine learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:14.889296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2c3fa338-f23a-4e07-9097-43483d498bdd · outbound

This paper cites Machine learning in ADHD and depression mental health diagnosis: A survey,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Machine learning in ADHD and depression mental health diagnosis: A survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:14.827854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5e1d42a5-0928-4682-8028-c61bab82b0aa · outbound

This paper cites Harnessing video intelligence: Intelligent system for adhd detection,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Harnessing video intelligence: Intelligent system for adhd detection,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:14.754753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ba7913db-3756-45a4-b08d-5a6e2b65682f · outbound

This paper cites Acoustic and text features analysis for adult ADHD screening: A data-driven approach utilizing diva interview,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Acoustic and text features analysis for adult ADHD screening: A data-driven approach utilizing diva interview,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:14.601914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a88f54c3-9d97-4c03-82d3-46423b8aaf02 · outbound

This paper cites Deep learning for depression recognition with audiovisual cues: A review,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Deep learning for depression recognition with audiovisual cues: A review,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:14.474801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 44b5a1ea-65cf-4f97-9f1c-3af2709492a7 · outbound

This paper cites Acoustical properties of speech as indicators of depression and suicidal risk,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Acoustical properties of speech as indicators of depression and suicidal risk,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:14.394836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c12f032a-a192-454d-b5a4-9eaaec0e7deb · outbound

This paper cites Automatic depression level detection via lp-norm pooling,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Automatic depression level detection via lp-norm pooling,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:14.275790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d7e57681-a91c-434d-a5db-2efeabee8b0c · outbound

This paper cites A hierarchical depression detection model based on vocal and emotional cues,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation A hierarchical depression detection model based on vocal and emotional cues,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:14.225206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 91008465-726b-4bcb-8a55-f4c397757e16 · outbound

This paper cites Position and orientation-aware one-shot learning for medical action recognition from signal data,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Position and orientation-aware one-shot learning for medical action recognition from signal data,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:14.190526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9f1e6346-3dec-45d8-bad5-6b20ecee721d · outbound

This paper cites Speechformer++: A hierarchical efficient framework for paralinguistic speech processing,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Speechformer++: A hierarchical efficient framework for paralinguistic speech processing,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:14.113548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fe689840-a74b-4976-b7a2-63ecc7e01bdb · outbound

This paper cites Wavdepressionnet: Automatic depression level prediction via raw speech signals,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Wavdepressionnet: Automatic depression level prediction via raw speech signals,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:13.964753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e1224455-565b-4dc2-9d1e-5f2bca12e9af · outbound

This paper cites Rabiner and B.-H.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Rabiner and B.-H

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T22:13:12.078454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db113268-63a5-4bb1-b40c-4f809a373c84 · outbound

This paper cites Tdca-net: Time-domain channel attention network for depression detection.,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Tdca-net: Time-domain channel attention network for depression detection.,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:13.714881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 961f98a8-b405-4509-b3d1-14b31146f2c9 · outbound

This paper cites Csenet: Complex squeeze-and- excitation network for speech depression level prediction,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Csenet: Complex squeeze-and- excitation network for speech depression level prediction,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:13.640857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 459dd75c-4e81-4468-9073-8fb67c449782 · outbound

This paper cites Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech separation,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech separation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:13.585965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation eec4d20e-b4ec-4db7-8f6c-aa1baeb4dd03 · outbound

This paper cites Attention is all you need in speech separation,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Attention is all you need in speech separation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:13.438406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fbc9b147-e458-42f8-818f-28b5d3248a7f · outbound

This paper cites Dual-path transformer network: Direct context-aware modeling for end-to-end monaural speech separation,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Dual-path transformer network: Direct context-aware modeling for end-to-end monaural speech separation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:13.390273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d337cd39-446b-4c02-9533-5072f3e6d373 · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Combining recurrent, convolutional, and continuous-time models with linear state space layers,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:13.315340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 00546175-ed13-4830-95f3-d40b76a550a6 · outbound

This paper cites Efficiently modeling long sequences with structured state spaces,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Efficiently modeling long sequences with structured state spaces,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:13.217506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c92a989c-498f-4f47-9fe7-764bad697b4a · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T22:13:12.227474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04c5f22a-0149-41f9-a863-c11e45e03a90 · outbound

This paper cites Mamba in Speech: Towards an Alternative to Self-Attention.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Mamba in Speech: Towards an Alternative to Self-Attention

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T22:13:12.242360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bff6a092-76f0-49e0-be87-ddbdaca021d5 · outbound

This paper cites Beyond self-attention: External attention using two linear layers for visual tasks,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Beyond self-attention: External attention using two linear layers for visual tasks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:13.147174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation feed49f8-b1ae-4422-8dcb-3db9f079e738 · outbound

This paper cites A VEC 2013: The Continu- ous Audio/Visual Emotion and Depression Recognition Challenge,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation A VEC 2013: The Continu- ous Audio/Visual Emotion and Depression Recognition Challenge,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:13.095540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 23c95c8e-b75d-491a-aeed-f007b82fdc96 · outbound

This paper cites A VEC 2014: 3D Dimensional Affect and De- pression Recognition Challenge,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation A VEC 2014: 3D Dimensional Affect and De- pression Recognition Challenge,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:12.991563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e3caaec0-7dc3-45eb-9e65-5646cf1a1d96 · outbound

This paper cites A narrative review of the beck depres- sion inventory (bdi) and implications for its use in an alcohol-dependent population,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation A narrative review of the beck depres- sion inventory (bdi) and implications for its use in an alcohol-dependent population,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:12.863380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6beb8513-8385-474f-907a-f7e829fdd25e · outbound

This paper cites Adam: A method for stochastic optimization,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Adam: A method for stochastic optimization,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:12.726550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 16897816-ba9d-441c-9044-46a27a17124c · outbound

This paper cites Attention is all you need,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Attention is all you need,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T22:13:12.421916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8af57a7d-18f2-4ca1-bca6-f23c7162cb62 · outbound

This paper cites Multimodal spatiotem- poral representation for automatic depression level detection,.

Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation Multimodal spatiotem- poral representation for automatic depression level detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:13:12.584751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.