Recent synthetic speech detectors leveraging the Transformer model have
superior performance compared to the convolutional neural network counterparts.
This improvement could be due to the powerful modeling ability of the
multi-head self-attention (MHSA) in the Transformer model, which learns the
temporal relationship of each input token. However, artifacts of synthetic
speech can be located in specific regions of both frequency channels and
temporal segments, while MHSA neglects this temporal-channel dependency of the
input sequence. In this work, we proposed a Temporal-Channel Modeling (TCM)
module to enhance MHSA's capability for capturing temporal-channel
dependencies. Experimental results on the ASVspoof 2021 show that with only
0.03M additional parameters, the TCM module can outperform the state-of-the-art
system by 9.25% in EER. Further ablation study reveals that utilizing both
temporal and channel information yields the most improvement for detecting
synthetic speech.

使用 Transformer 模型，通过引入 Temporal-Channel Modeling（TCM）模块来增强 multi-head self-attention（MHSA）对于捕捉时域 - 频域依赖关系的能力，以提升合成语音检测效果。在 ASVspoof 2021 数据集上进行的实验表明，仅使用 0.03M 额外参数的 TCM 模块，在等误拒曲线（EER）指标上超过了当前最先进系统 9.25% 的性能。进一步的消融研究显示，同时利用时域和频域信息对于检测合成语音效果最好。

多头自注意力中的时间通道建模用于合成语音检测

Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic  Speech Detection

With recent advances in speech synthesis including text-to-speech (TTS) and
voice conversion (VC) systems enabling the generation of ultra-realistic audio
deepfakes, there is growing concern about their potential misuse. However, most
deepfake (DF) detection methods rely solely on the fuzzy knowledge learned by a
single model, resulting in performance bottlenecks and transparency issues.
Inspired by retrieval-augmented generation (RAG), we propose a
retrieval-augmented detection (RAD) framework that augments test samples with
similar retrieved samples for enhanced detection. We also extend the
multi-fusion attentive classifier to integrate it with our proposed RAD
framework. Extensive experiments show the superior performance of the proposed
RAD framework over baseline methods, achieving state-of-the-art results on the
ASVspoof 2021 DF set and competitive results on the 2019 and 2021 LA sets.
Further sample analysis indicates that the retriever consistently retrieves
samples mostly from the same speaker with acoustic characteristics highly
consistent with the query audio, thereby improving detection performance.

使用检索增强的检测框架，基于语音合成和深假声音技术，以及 ASVspoof 2021 数据集的实验结果，改进了深假声音检测方法，并提升了检测性能。

检索辅助的音频深度伪造检测

Retrieval-Augmented Audio Deepfake Detection

ASVspoof 2021 is the forth edition in the series of bi-annual challenges
which aim to promote the study of spoofing and the design of countermeasures to
protect automatic speaker verification systems from manipulation. In addition
to a continued focus upon logical and physical access tasks in which there are
a number of advances compared to previous editions, ASVspoof 2021 introduces a
new task involving deepfake speech detection. This paper describes all three
tasks, the new databases for each of them, the evaluation metrics, four
challenge baselines, the evaluation platform and a summary of challenge
results. Despite the introduction of channel and compression variability which
compound the difficulty, results for the logical access and deepfake tasks are
close to those from previous ASVspoof editions. Results for the physical access
task show the difficulty in detecting attacks in real, variable physical
spaces. With ASVspoof 2021 being the first edition for which participants were
not provided with any matched training or development data and with this
reflecting real conditions in which the nature of spoofed and deepfake speech
can never be predicated with confidence, the results are extremely encouraging
and demonstrate the substantial progress made in the field in recent years.

ASVspoof 2021 提出了新的 deepfake 语音检测任务，介绍了三个任务、每个任务的新数据库、评估指标、四个基线模型、评估平台以及比赛结果，并展示了近年来该领域取得的显著进展，尽管物理访问任务的结果显示在现实、不断变化的物理空间中检测攻击的难度很大。