Current synthetic speech detection (SSD) methods perform well on specific datasets but require improvement in interpretability and robustness. One possible reason is the lack of interpretability analysis of synthetic speech defects. In this paper, the flaws in the temporal consistency (TC) of speaker features inherent in the speech synthesis process are analyzed. Differences in the TC of intra-utterance speaker features arise due to limited control over speaker features during speech synthesis. The speech generated by text-to-speech algorithms exhibits higher TC, while the speech generated by voice conversion algorithms yeilds slightly lower TC compared to bonafide speech. Based on this finding, a new SSD method based on the TC of speaker features is proposed. Modeling the TC of intra-utterance speaker features extracted by a pre-trained ASV system can be used for SSD. The proposed method achieves equal error rates of 0.84%, 3.93%, 12.98% and 24.66% on the ASVspoof 2019 LA, 2021 LA, 2021 DF and IntheWild evaluation datasets, respectively, demonstrating strong interpretability and robustness.

Self-supervised learning has shown promising performance on speaker verification tasks, among which Self DIstillation with NO labels (DINO) is currently a widely adopted framework. As one of the unsupervised deep clustering methods, the number of valid prototypes in DINO is far less than the speakers in practical applications and remains unchanged throughout the training period, leading to severe speaker confusion and performance degradation. Therefore, a strategy named prototype division (PD) is proposed to iteratively generate fine-grained prototypes in the projection space based on the converged model to separate confused categories, where new prototypes are derived from the neighborhood of the existing valid prototypes by clustering or sampling. The results on Vox1O achieve significant improvements, relatively outperforming the baseline by 31.1% without any auxiliary loss. Further experiments on CN-Celeb also show stable improvement, proving the consistency of the proposed method.

The current speech anti-spoofing countermeasures (CMs) show excellent performance on specific datasets. However, removing the silence of test speech through Voice Activity Detection (VAD) can severely degrade performance. In this paper, the impact of silence on speech anti-spoofing is analyzed. First, the reasons for the impact are explored, including the proportion of silence duration and the content of silence. The proportion of silence duration in spoof speech generated by text-to-speech (TTS) algorithms is lower than that in bonafide speech. And the content of silence generated by different waveform generators varies compared to bonafide speech. Then the impact of silence on model prediction is explored. Even after retraining, the spoof speech generated by neural network based end-to-end TTS algorithms suffers a significant rise in error rates when the silence is removed. To demonstrate the reasons for the impact of silence on CMs, the attention distribution of a CM is visualized through class activation mapping (CAM). Furthermore, the implementation and analysis of the experiments masking silence or non-silence demonstrates the significance of the proportion of silence duration for detecting TTS and the importance of silence content for detecting voice conversion (VC). Based on the experimental results, improving the robustness of CMs against unknown spoofing attacks by masking silence is also proposed. Finally, the attacks on anti-spoofing CMs through concatenating silence, and the mitigation of VAD and silence attack through low-pass filtering are introduced.

Based on the assumption that there is a correlation between anti-spoofing and speaker verification, a Total-Divide-Total integrated Spoofing-Aware Speaker Verification (SASV) system based on pre-trained automatic speaker verification (ASV) system and integrated scoring module is proposed and submitted to the SASV 2022 Challenge.The training and scoring of ASV and anti-spoofing countermeasure (CM) in current SASV systems are relatively independent, ignoring the correlation.In this paper, by leveraging the correlation between the two tasks, an integrated SASV system can be obtained by simply training a few more layers on the basis of the baseline pre-trained ASV subsystem.The features in pre-trained ASV system are utilized for logical access spoofing speech detection.Further, speaker embeddings extracted by the pre-trained ASV system are used to improve the performance of the CM.The integrated scoring module takes the embeddings of the ASV and anti-spoofing branches as input and preserves the correlation between the two tasks through matrix operations to produce integrated SASV scores.Submitted primary system achieved equal error rate (EER) of 3.07% on the development dataset of the SASV 2022 Challenge and 4.30% on the evaluation part, which is a 25% improvement over the baseline systems.