Teffic-Audio: Tell Fact from Fiction
- URL: http://arxiv.org/abs/2607.28351v1
- Date: Thu, 30 Jul 2026 15:21:38 GMT
- Title: Teffic-Audio: Tell Fact from Fiction
- Abstract summary: This report presents Teffic-Audio, a general speech deepfake detection system for comprehensive evaluation environment.<n>Teffic-Audio adopts a straightforward detector architecture consisting of a Conformer-based speech encoder, multi-head attentive statistics pooling, and a binary classifier.<n>Teffic-Audio achieves a pooled EER of 1.454% on the 14 test sets of Speech-DF-Arena, outperforming all currently public systems on the leaderboard.
- Score: 15.841023625898053
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Speech deepfake detection has expanded in scope with increasingly heterogeneous spoofing mechanisms, including speech synthesis, voice conversion, vocoder reconstruction, and neural-codec resynthesis. The resulting spoofing artifacts can be further shaped by variability in source speech, recording environments, and transmission channels. This variability makes robust generalization across heterogeneous conditions a central requirement for practical detection systems. This report presents Teffic-Audio, a general speech deepfake detection system designed for comprehensive evaluation environment. Teffic-Audio adopts a straightforward detector architecture consisting of a Conformer-based speech encoder, multi-head attentive statistics pooling, and a binary classifier. Rather than relying on additional architectural complexity, the system improves generalization through its training recipe, which integrates multi-source data, attack- and source-balanced sampling, and diverse audio augmentation. Trained only with open-source data, Teffic-Audio achieves a pooled EER of 1.454% on the 14 test sets of Speech-DF-Arena, outperforming all currently public systems on the leaderboard. It also obtains the lowest EER on five individual test sets and shows a favorable performance-complexity trade-off compared with larger leading systems. Overall, Teffic-Audio provides a strong and practical reference system for general speech deepfake detection.
Related papers
- AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan [64.09595490689874]
We propose the All-Type Audio Deepfake Detection (AT-ADD) Grand Challenge for ACM Multimedia 2026.<n>AT-ADD aims to accelerate the development of robust and generalizable audio forensic technologies.
arXiv Detail & Related papers (2026-04-09T12:38:19Z) - AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worlds [38.75029700407531]
AUDETER is a large-scale, highly diverse deepfake audio dataset.<n>It consists of over 4,500 hours of synthetic audio generated by 11 recent TTS models and 10 vocoders with a broad range of TTS/vocoder patterns.<n>It is the largest deepfake audio dataset by scale.
arXiv Detail & Related papers (2025-09-04T16:03:44Z) - Exposing Synthetic Speech: Model Attribution and Detection of AI-generated Speech via Audio Fingerprints [11.703509488782345]
We introduce a training-free, yet effective approach for detecting AI-generated speech.<n>We tackle three key tasks: (1) single-model attribution in an open-world setting, (2) multi-model attribution in a closed-world setting, and (3) detection of synthetic versus real speech.
arXiv Detail & Related papers (2024-11-21T10:55:49Z) - Where are we in audio deepfake detection? A systematic analysis over generative and detection models [59.09338266364506]
SONAR is a synthetic AI-Audio Detection Framework and Benchmark.<n>It provides a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content.<n>It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based detection systems.
arXiv Detail & Related papers (2024-10-06T01:03:42Z) - Training-Free Deepfake Voice Recognition by Leveraging Large-Scale Pre-Trained Models [52.04189118767758]
Generalization is a main issue for current audio deepfake detectors.
In this paper we study the potential of large-scale pre-trained models for audio deepfake detection.
arXiv Detail & Related papers (2024-05-03T15:27:11Z) - Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation
and Recognition [52.11964238935099]
An audio-visual multi-channel speech separation, dereverberation and recognition approach is proposed in this paper.
Video input is consistently demonstrated in mask-based MVDR speech separation, DNN-WPE or spectral mapping (SpecM) based speech dereverberation front-end.
Experiments were conducted on the mixture overlapped and reverberant speech data constructed using simulation or replay of the Oxford LRS2 dataset.
arXiv Detail & Related papers (2023-07-06T10:50:46Z) - Combining Automatic Speaker Verification and Prosody Analysis for
Synthetic Speech Detection [15.884911752869437]
We present a novel approach for synthetic speech detection, exploiting the combination of two high-level semantic properties of the human voice.
On one side, we focus on speaker identity cues and represent them as speaker embeddings extracted using a state-of-the-art method for the automatic speaker verification task.
On the other side, voice prosody, intended as variations in rhythm, pitch or accent in speech, is extracted through a specialized encoder.
arXiv Detail & Related papers (2022-10-31T11:03:03Z) - Fully Automated End-to-End Fake Audio Detection [57.78459588263812]
This paper proposes a fully automated end-toend fake audio detection method.
We first use wav2vec pre-trained model to obtain a high-level representation of the speech.
For the network structure, we use a modified version of the differentiable architecture search (DARTS) named light-DARTS.
arXiv Detail & Related papers (2022-08-20T06:46:55Z) - Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence
Modeling [61.351967629600594]
This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq), non-parallel voice conversion approach.
In this approach, we combine a bottle-neck feature extractor (BNE) with a seq2seq synthesis module.
Objective and subjective evaluations show that the proposed any-to-many approach has superior voice conversion performance in terms of both naturalness and speaker similarity.
arXiv Detail & Related papers (2020-09-06T13:01:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.