AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks | Paper Citation Record

Observation afdcb53f-4466-4d38-bc83-faf75effdfb7 · outbound

This paper cites Busso and R.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Busso and R

Reference 1

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This paper cites Artificial Intelligence Review , volume=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Artificial Intelligence Review , volume=

Reference 2

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This paper cites Emoanti: audio anti-deepfake with refined emotion-guided representations.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Emoanti: audio anti-deepfake with refined emotion-guided representations

Reference 3

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This paper cites Applied intelligence , volume=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Applied intelligence , volume=

Reference 4

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This paper cites The 2024 ACM Conference on Fairness, Accountability, and Transparency , pages=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks The 2024 ACM Conference on Fairness, Accountability, and Transparency , pages=

Reference 5

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This paper cites End-to-End anti-spoofing with RawNet2 , year=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks End-to-End anti-spoofing with RawNet2 , year=

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This paper cites AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks , year=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks , year=

Reference 7

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Observation bf934a49-aa58-4cdd-8b47-163843f32368 · outbound

This paper cites ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech , journal =.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech , journal =

Reference 8

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This paper cites ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild , year=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild , year=

Reference 9

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This paper cites StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models , url =.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models , url =

Reference 10

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This paper cites Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 11

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This paper cites CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens

Reference 12

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This paper cites ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale

Reference 13

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This paper cites China National Conference on Chinese Computational Linguistics , pages=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks China National Conference on Chinese Computational Linguistics , pages=

Reference 14

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ADD 2022: the first Audio Deep Synthesis Detection Challenge , year=

Reference 15

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

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ADD 2023: the Second Audio Deepfake Detection Challenge

Reference 16

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Training , volume=

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This paper cites Seen and Unseen Emotional Style Transfer for Voice Conversion with A New Emotional Speech Dataset , year=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Seen and Unseen Emotional Style Transfer for Voice Conversion with A New Emotional Speech Dataset , year=

Reference 18

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This paper cites The Twelfth International Conference on Learning Representations , year=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks The Twelfth International Conference on Learning Representations , year=

Reference 19

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This paper cites ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 20

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This paper cites Advances in Neural Information Processing Systems , volume=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Advances in Neural Information Processing Systems , volume=

Reference 21

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Development , volume=

Reference 22

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Interspeech 2022 , year=

Reference 23

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This paper cites Light Convolutional Neural Network with Feature Genuinization for Detection of Synthetic Speech Attacks.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Light Convolutional Neural Network with Feature Genuinization for Detection of Synthetic Speech Attacks

Reference 24

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks

Reference 25

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks 2023 18th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) , pages=

Reference 26

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This paper cites The MSP-Podcast Corpus.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks The MSP-Podcast Corpus

Reference 27

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks EMOQ-TTS: Emotion Intensity Quantization for Fine-Grained Controllable Emotional Text-to-Speech , year=

Reference 28

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Emodiff: Intensity Controllable Emotional Text-to-Speech with Soft-Label Guidance , year=

Reference 29

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ED-TTS: Multi-Scale Emotion Modeling Using Cross-Domain Emotion Diarization for Emotional Speech Synthesis , year=

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers , year=

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Advances in Neural Information Processing Systems , editor=

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Thirty-seventh Conference on Neural Information Processing Systems , year=

Reference 33

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Laugh Now Cry Later: Controlling Time-Varying Emotional States of Flow-Matching-Based Zero-Shot Text-To-Speech , year=

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 35

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Proceedings of the 32nd ACM International Conference on Multimedia , pages=

Reference 36

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks IEEE Journal of Selected Topics in Signal Processing , volume=

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Advances in neural information processing systems , volume=

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks IEEE/ACM transactions on audio, speech, and language processing , volume=

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Pitch Imperfect: Detecting Audio Deepfakes Through Acoustic Prosodic Analysis

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Unresolved cited work

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks , author=

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks EURASIP Journal on Audio, Speech, and Music Processing , volume=

Reference 45

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Frontiers in signal processing , volume=

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks IEICE TRANSACTIONS on Information and Systems , volume=

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks IEEE/ACM Transactions on Audio, Speech, and Language Processing , year=

Reference 49

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Unresolved cited work

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Textless Speech Emotion Conversion using Discrete and Decomposed Representations

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks IEEE/ACM Transactions on Audio, Speech, and Language Processing , year=

Reference 52

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection , year=

Reference 53

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Unresolved cited work

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This paper cites Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter , pages=.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter , pages=

Reference 55

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Better Be Computer or I'm Dumb

Reference 56

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Reference 57

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Reference 58

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 59

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Reference 60

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Reference 61

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks , author=

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Reference 63

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Reference 64

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Observation e2505c97-f69b-4ee7-9973-2bab71d4adec · outbound

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 65

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 66

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Observation dc0b567b-d012-4b43-a87c-6dd78c911155 · outbound

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Reference 67

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 68

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Observation 0a3cac74-4902-4a2e-a732-eefab1a784bd · outbound

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Proceedings of the 31st ACM International Conference on Multimedia , pages=

Reference 69

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Observation 5de3e80a-8fdc-4edf-9520-b28e52cf96b3 · outbound

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Reference 70

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Reference 71

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks IEEE Transactions on Affective Computing , volume=

Reference 72

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 73

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Diff-HierVC: Diffusion-based Hierarchical Voice Conversion with Robust Pitch Generation and Masked Prior for Zero-shot Speaker Adaptation

Reference 74

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks 2024 IEEE Spoken Language Technology Workshop (SLT) , pages=

Reference 75

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 76

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Language, Cognition and Neuroscience , volume=

Reference 77

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Studies in second language acquisition , volume=

Reference 78

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks and Li, Haizhou , journal=

Reference 79

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Librispeech: An ASR corpus based on public domain audio books , year=

Reference 80

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Observation 06b115c3-1103-4309-97cf-00dc96bdafb0 · outbound

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks International Conference on Pattern Recognition , pages=

Reference 81

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP) , pages=

Reference 82

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) , pages=

Reference 83

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks METTS: Multilingual Emotional Text-to-Speech by Cross-Speaker and Cross-Lingual Emotion Transfer , year=

Reference 84

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Generalizable Audio Spoofing Detection using Non-Semantic Representations

Reference 85

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Observation de3d97ca-3d60-43da-afcb-a4c14f2dcade · outbound

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks IEEE Transactions on Affective Computing , volume=

Reference 86

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks 2024 International Joint Conference on Neural Networks (IJCNN) , pages=

Reference 87

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Observation d613ccc7-39bb-413c-9efa-702eee1fbc48 · outbound

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks An explainability study of the constant Q cepstral coefficient spoofing countermeasure for automatic speaker verification

Reference 88

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks EURASIP Journal on Information Security , volume=

Reference 89

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Observation 252754f3-e96b-4bcc-8f07-8538e7a122ec · outbound

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks IEEE/ACM Transactions on Audio, Speech, and Language Processing , volume=

Reference 90

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Observation 7631c976-57bd-4421-9c04-22fa68bd1068 · outbound

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Speech is Silver, Silence is Golden: What do ASVspoof-trained Models Really Learn?

Reference 91

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Feature Genuinization based Residual Squeeze-and-Excitation for Audio Anti-Spoofing in Sound AI , year=

Reference 92

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks PloS one , volume=

Reference 93

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks International Conference on Machine Learning , year=

Reference 94

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Observation 93d279d8-29c1-4fd5-afc8-1017556a9f8e · outbound

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model

Reference 95

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 96

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-08T11:50:21.162778Z

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Observation 07650bfa-c611-42d6-88ef-957cae3bd642 · outbound

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme

Reference 98

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks , author=

Reference 99

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AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Advances in Neural Information Processing Systems , volume=

Reference 100

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Pith citing papers