Paper Citation Record · LEDGER

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models

As of 14 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2507.12595.

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

pith.paper-citation-record.v1
2507.12595 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:49:30.258365Z

measured 36 of 36 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:49:26.014563Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T00:34:47.280797Z

Reference resolution

34 of 34 outbound references displayed

verified exact0
verified fuzzy26
unresolved7
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fbc77585-a1fe-47c1-b85c-87d774e6a193 · outbound

This paper cites an unresolved cited work.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:49:37.870541Z

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 c6afa01a-ab22-473e-b564-6bced7e1a1c4 · outbound

This paper cites Speech Foundation Models (SFMs): For monolingual SFMs, we consider WavLM1 [17], Unispeech-SAT2 [18], Wav2vec23 [19], and HuBERT 4 [20].

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Speech Foundation Models (SFMs): For monolingual SFMs, we consider WavLM1 [17], Unispeech-SAT2 [18], Wav2vec23 [19], and HuBERT 4 [20]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:37.590706Z

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 4b421cee-1121-465c-ba68-e3780698981b · outbound

This paper cites We use Fully Connected Network (FCN) and CNN as downstream networks with individual FMs.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models We use Fully Connected Network (FCN) and CNN as downstream networks with individual FMs

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:37.302061Z

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 ab3c6371-0fd2-4e86-b964-08bcc5943917 · outbound

This paper cites Benchmark Dataset We use the only dataset available for EFD by Zhao et al.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Benchmark Dataset We use the only dataset available for EFD by Zhao et al

Reference 4

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T16:49:36.792819Z

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 f4bbbe07-ed8e-4892-8c2f-4283e552552b · outbound

This paper cites The projected features are then fused using Tucker Decomposi- tion, where a core tensor T ∈ Rdf ×df ×df models the high- order interactions between features.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models The projected features are then fused using Tucker Decomposi- tion, where a core tensor T ∈ Rdf ×df ×df models the high- order interactions between features

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:37.062031Z

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 1f3bb06b-db1f-447d-adb1-9834f0f44d1f · outbound

This paper cites ADD 2023: the Second Audio Deepfake Detection Challenge.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models ADD 2023: the Second Audio Deepfake Detection Challenge

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:49:27.397372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 838739be-3923-4839-af3a-dd0078da5c28 · outbound

This paper cites Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T16:49:26.014563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76b2a696-fe67-4852-a007-feb5df332f39 · outbound

This paper cites Our comprehen- sive analysis confirms their superiority in both in-domain and cross-lingual settings.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Our comprehen- sive analysis confirms their superiority in both in-domain and cross-lingual settings

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:36.542799Z

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 e1fffa2e-cbb1-49a2-b498-23fdfc41ebce · outbound

This paper cites ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures chal- lenge,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures chal- lenge,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:36.198859Z

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 2f882a3c-4ea7-4048-9c4a-462dd9fb6a8e · outbound

This paper cites The ASVspoof 2017 Challenge: Assessing the Limits of Replay Spoofing Attack Detection,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models The ASVspoof 2017 Challenge: Assessing the Limits of Replay Spoofing Attack Detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:35.831072Z

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 53172660-5363-45da-b2f6-4598abf9d420 · outbound

This paper cites ASVspoof 2019: Fu- ture Horizons in Spoofed and Fake Audio Detection,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models ASVspoof 2019: Fu- ture Horizons in Spoofed and Fake Audio Detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:35.480805Z

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 4d010a09-e6a8-4e5c-8de9-ff4edd2db105 · outbound

This paper cites ASVspoof 2021: ac- celerating progress in spoofed and deepfake speech detection,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models ASVspoof 2021: ac- celerating progress in spoofed and deepfake speech detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:35.200538Z

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 8548c05f-232e-4091-a30a-012582ab1980 · outbound

This paper cites Add 2022: the first audio deep synthesis detection challenge,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Add 2022: the first audio deep synthesis detection challenge,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:34.893476Z

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 66316a3a-d344-4529-a0f6-9e974b92de00 · outbound

This paper cites MLAAD: The Multi-Language Audio Anti-Spoofing Dataset.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models MLAAD: The Multi-Language Audio Anti-Spoofing Dataset

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T16:49:27.535796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eadbfb0d-e27c-4bf1-a4e6-29ccd3c45bac · outbound

This paper cites Singfake: Singing voice deepfake detection,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Singfake: Singing voice deepfake detection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:34.631536Z

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 4822b577-e0bf-4caf-a6c1-a31d2bfe7d32 · outbound

This paper cites Scenefake: An initial dataset and benchmarks for scene fake audio detection,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Scenefake: An initial dataset and benchmarks for scene fake audio detection,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:34.428750Z

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 d7689fa9-ed8b-4ef2-bf0c-31dd2530f029 · outbound

This paper cites Emofake: An initial dataset for emotion fake audio detection,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Emofake: An initial dataset for emotion fake audio detection,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:34.153219Z

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 b5a831cd-7e32-40a1-8fd3-a867dd034679 · outbound

This paper cites Im- proved deepfake detection using whisper features,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Im- proved deepfake detection using whisper features,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:33.906117Z

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 ae5fd083-1d83-417c-b53f-85e2eed824d9 · outbound

This paper cites Detection of deepfake environmental audio,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Detection of deepfake environmental audio,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:33.636322Z

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 dd028d11-dce9-48d8-b051-ed2a0c04ffb4 · outbound

This paper cites Exploring wavlm back-ends for speech spoofing and deepfake detection,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Exploring wavlm back-ends for speech spoofing and deepfake detection,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:33.406192Z

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 4c1a7829-1fc2-4c9c-a498-e1803d486d42 · outbound

This paper cites Investi- gation of ensemble features of self-supervised pretrained models for automatic speech recognition,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Investi- gation of ensemble features of self-supervised pretrained models for automatic speech recognition,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:33.244477Z

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 66170b6b-a1da-442c-ba5b-d78e9a4c033f · outbound

This paper cites Investigation of ensemble of self-supervised models for speech emotion recognition,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Investigation of ensemble of self-supervised models for speech emotion recognition,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:32.968592Z

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 83c91593-4ab3-4cfa-a532-fbe584180d6a · outbound

This paper cites Heterogeneity over homogeneity: Investigating multilingual speech pre-trained models for detecting audio deepfake,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Heterogeneity over homogeneity: Investigating multilingual speech pre-trained models for detecting audio deepfake,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:32.859480Z

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 ce4baea6-bcc6-43a0-8d59-e25da3c9d3de · outbound

This paper cites Wavlm: Large-scale self-supervised pre-training for full stack speech processing,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Wavlm: Large-scale self-supervised pre-training for full stack speech processing,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:32.560832Z

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 36ba3077-b2ea-4445-8f2a-233d81d0766d · outbound

This paper cites Unispeech-sat: Universal speech repre- sentation learning with speaker aware pre-training,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Unispeech-sat: Universal speech repre- sentation learning with speaker aware pre-training,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:32.168224Z

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 562ed369-5747-4e20-87d4-ac1b77ba64f0 · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:31.723379Z

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 0ea59450-ff9b-4d66-8433-ba7ee0ee6e93 · outbound

This paper cites Hubert: Self-supervised speech representation learning by masked prediction of hidden units,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Hubert: Self-supervised speech representation learning by masked prediction of hidden units,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:31.606210Z

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 3c295996-d049-4d4e-a4bc-c5a57ab2a60d · outbound

This paper cites Xls-r: Self-supervised cross-lingual speech representation learning at scale,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Xls-r: Self-supervised cross-lingual speech representation learning at scale,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:31.457878Z

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 45a42136-71e2-499a-a8ea-d9423a94dd5e · outbound

This paper cites Robust speech recognition via large-scale weak supervision,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Robust speech recognition via large-scale weak supervision,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T16:49:29.545716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce83244b-cd4a-4fe5-898d-f3d462781914 · outbound

This paper cites Scaling speech technology to 1,000+ languages,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models Scaling speech technology to 1,000+ languages,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:31.178127Z

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 72f10ea0-7940-41d3-8853-067124a60b30 · outbound

This paper cites X-vectors: Robust dnn embeddings for speaker recognition,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models X-vectors: Robust dnn embeddings for speaker recognition,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:30.905187Z

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 115fbe28-5486-4ddd-bc13-8db3b3627d03 · outbound

This paper cites X- vectors meet emotions: A study on dependencies between emotion and speaker recognition,.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models X- vectors meet emotions: A study on dependencies between emotion and speaker recognition,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:30.529996Z

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 8bb2fca9-b7d9-4b04-8130-381de20ba017 · outbound

This paper cites MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T16:49:30.038796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ae0d372-7c74-45ee-999e-cd7dff9e041a · outbound

This paper cites MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning.

Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T16:49:30.258365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 838739be-3923-4839-af3a-dd0078da5c28 · inbound