Rapid advancements in speech synthesis and voice conversion bring convenience but also new security risks, creating an urgent need for effective audio deepfake detection. Although current models perform well, their effectiveness diminishes when confronted with the diverse and evolving nature of real-world deepfakes. To address this issue, we propose a continual learning method named Region-Based Optimization (RegO) for audio deepfake detection. Specifically, we use the Fisher information matrix to measure important neuron regions for real and fake audio detection, dividing them into four regions. First, we directly fine-tune the less important regions to quickly adapt to new tasks. Next, we apply gradient optimization in parallel for regions important only to real audio detection, and in orthogonal directions for regions important only to fake audio detection. For regions that are important to both, we use sample proportion-based adaptive gradient optimization. This region-adaptive optimization ensures an appropriate trade-off between memory stability and learning plasticity. Additionally, to address the increase of redundant neurons from old tasks, we further introduce the Ebbinghaus forgetting mechanism to release them, thereby promoting the model’s ability to learn more generalized discriminative features. Experimental results show our method achieves a 21.3 percent improvement in EER over the state-of-the-art continual learning approach RWM for audio deepfake detection. Moreover, the effectiveness of RegO extends beyond the audio deepfake detection domain, showing potential significance in other tasks, such as image recognition.

The rapid evolution of speech synthesis and voice conversion has raised substantial concerns due to the potential misuse of such technology, prompting a pressing need for effective audio deepfake detection mechanisms. Existing detection models have shown remarkable success in discriminating known deepfake audio, but struggle when encountering new attack types. To address this challenge, one of the emergent effective approaches is continual learning. In this paper, we propose a continual learning approach called Radian Weight Modification (RWM) for audio deepfake detection. The fundamental concept underlying RWM involves categorizing all classes into two groups: those with compact feature distributions across tasks, such as genuine audio, and those with more spread-out distributions, like various types of fake audio. These distinctions are quantified by means of the in-class cosine distance, which subsequently serves as the basis for RWM to introduce a trainable gradient modification direction for distinct data types. Experimental evaluations against mainstream continual learning methods reveal the superiority of RWM in terms of knowledge acquisition and mitigating forgetting in audio deepfake detection. Furthermore, RWM's applicability extends beyond audio deepfake detection, demonstrating its potential significance in diverse machine learning domains such as image recognition.

Language model based text-to-speech (TTS) models, like VALL-E, have gained attention for their outstanding in-context learning capability in zero-shot scenarios. Neural speech codec is a critical component of these models, which can convert speech into discrete token representations. However, excessive token sequences from the codec may negatively affect prediction accuracy and restrict the progression of Language model based TTS models. To address this issue, this paper proposes a novel neural speech codec with time-invariant codes named TiCodec. By encoding and quantizing time-invariant information into a separate code, TiCodec can reduce the amount of frame-level information that needs encoding, effectively decreasing the number of tokens as codes of speech. Furthermore, this paper introduces a time-invariant encoding consistency loss to enhance the consistency of time-invariant code within an utterance, which can benefit the zero-shot TTS task. Experimental results demonstrate that TiCodec can not only enhance the quality of reconstruction speech with fewer tokens but also increase the similarity and naturalness, as well as reduce the word error rate of the synthesized speech by the TTS model. The code is publicly available at https://github.com/y-ren16/TiCodec.

With the widespread application of Automatic Speaker Verification (ASV) technology in security authentication, the threat of fake audio attacks looms as a malicious means compromising system security. In this study, we employ the multi-scale permutation entropy (MPE) in audio deepfake detection, which could help measure the complexity and detect the dynamic characteristics of audio signals at different scales. Experimental results indicate that MPE can effectively improve the performance of LFCC. For example, on the ASVspoof2019 LA test set, it successfully achieves an equal error rate (EER) of less than 2%, which is around 50% lower than that of LFCC. Notably, MPE exhibits extraordinary generalization performance when applied to the In-the-Wild dataset, as its performance of EER is comparable to that of Wav2vec, without requiring pretraining. Therefore, we believe that MPE holds promising prospects in voice biometric recognition for anti-spoofing applications. Our code is available at https://github.com/ADDchallenge/MPE-for-audio-deepfake-detection

Current fake audio detection algorithms have achieved promising performances on most datasets. However, their performance may be significantly degraded when dealing with audio of a different dataset. The orthogonal weight modification to overcome catastrophic forgetting does not consider the similarity of genuine audio across different datasets. To overcome this limitation, we propose a continual learning algorithm for fake audio detection to overcome catastrophic forgetting, called Regularized Adaptive Weight Modification (RAWM). When fine-tuning a detection network, our approach adaptively computes the direction of weight modification according to the ratio of genuine utterances and fake utterances. The adaptive modification direction ensures the network can effectively detect fake audio on the new dataset while preserving its knowledge of old model, thus mitigating catastrophic forgetting. In addition, genuine audio collected from quite different acoustic conditions may skew their feature distribution, so we introduce a regularization constraint to force the network to remember the old distribution in this regard. Our method can easily be generalized to related fields, like speech emotion recognition. We also evaluate our approach across multiple datasets and obtain a significant performance improvement on cross-dataset experiments.

Prosodic boundaries are still crucial to the naturalness of end-to-end speech synthesis systems. This article proposes to use adversarial multi-task learning to predict prosodic boundaries. Adversarial multi-task learning is utilized to transfer knowledge from an auxiliary POS tagging task to a prosodic boundary prediction task. Furthermore, multi-modal embeddings are composed of contextual word and speech embedding features obtained from the pre-trained bidirectional encoder representations from transformers (BERT) model and Speech2Vec. We can utilize linguistic and acoustic information from large amounts of external text and speech data without prosodic boundary labels. At the inference stage, the prosodic boundary predicting model can use the syntactic features learnt from the POS tagging task without any extra computation cost due to only employing the prosodic boundary predicting task to decode. We conducted experiments on Mandarin datasets. The results show that the models using multi-modal embeddings from the pre-trained BERT and Speech2Vec outperform the models trained with single modal embedding. Furthermore, the models trained with adversarial training obtain further performance gains by up to 2.95% in$F_{1}$score.