In recent years, the field of talking head generation has made significant strides. However, the need for substantial computational resources for model training, coupled with a scarcity of high-quality video data, poses challenges for the rapid customization of model to specific individual. Additionally, existing models usually only support single-modal control, lacking the ability to generate vivid facial expressions and controllable head poses based on multiple conditions such as audio, video, etc. These limitations restricts the models' widespread application. In this paper, we introduce a two-stage method called Control-Talker to achieve rapid customization of identity in talking head model and high-quality generation based on multimodal conditions. Specifically, we divide the training process into two stages: prior learning stage and identity rapid-customization stage. 1) In the prior learning stage, we leverage a diffusion-based model pre-trained on the high-quality image dataset to acquire a robust controllable facial prior. Meanwhile, we innovatively propose a high-frequency ControlNet structure to enhance the fidelity of the synthesized results. This structure adeptly extracts a high-frequency feature map from the source image, serving as a facial texture prior, thereby excellently preserving facial texture of the source image. 2) In the identity rapid-customization stage, the identity is fixed by fine-tuning the U-Net part of the diffusion model on merely several images of a specific individual. The entire fine-tuning process for identity customization can be completed within approximately ten minutes, thereby significantly reducing training costs. Further, we propose a unified driving method for both audio and video, enabling the model to precisely control expressions, poses, and lighting under multi conditions. Extensive experiments and visual results demonstrate that our method outperforms other state-of-the-art models. Additionally, our model demonstrates reduced training costs and lower data requirements.

A major difficulty in speech spoofing detection lies in improving the generalization ability to detect unknown forgery methods. However, most previous methods do not consider the interference of silence information on the generalization performance of speech spoofing detection. Notably, we experimentally observe that the generalization performance of existing methods drops sharply when silence segments are trimmed. This indicates that previous works have two problems: a) they do not remove the interference of silence and over-rely on silence information, and b) they lack the ability to uncover general forgery traces in utterance segments. To solve the above two problems, we propose a novel Silence-Agnostic Speech Spoofing Detection (SASSD) framework. To be specific, unlike previous methods trained on speech samples with silence information, we completely remove the leading and trailing silence segments from all speech samples to eliminate the interference of silence and focus on utterance information. Meanwhile, to uncover general forgery traces in utterance segments and improve the generalization ability, we view speech spoofing detection as a domain generalization problem and employ meta-learning to simulate the actual domain shift scenarios, which can reduce overfitting to specific forgery methods. In addition, to improve the domain generalization of meta-learning, a novel data augmentation method named ShuffleMix is proposed. Unlike previous methods that only consider inter-speech patterns, our method additionally introduces an intra-speech augmentation technique, which performs enhancements within a single speech and across multiple speech to generate more diverse forged samples. Extensive experiments show that our method achieves SOTA on the ASVspoof 2019LA dataset. In particular, our method achieves 0.231% EER and 2.529% EER on the original dataset with silence information and the silence-trimmed dataset, respectively.