Speaker verification technology has widespread applications across many domains, benefiting from deep learning advancements. However, due to the high cost of acquiring labeled data, semi-supervised learning has emerged as a prominent research focus. Current semi-supervised learning frameworks commonly suffer from two limitations: (1) the labeled data distribution is often restricted, and (2) they still rely on a considerable amount of labeled data. To address these issues, we propose three different distribution scenarios of labeled data and construct a general semi-supervised framework. Furthermore, to enhance the guidance efficacy of limited labeled data, we innovatively employ prototype space optimization to strengthen the model's discriminative capability under low-resource scenarios. Experimental results demonstrate that on the Vox1-o test set, our approach achieves a 41.7% relative reduction in equal error rate compared to self-supervised baselines, and a 28.9% improvement over conventional semi-supervised framework baselines.

Large language models (LLMs) often exhibit limited performance on domain-specific tasks due to the natural disproportionate representation of specialized information in their training data and the static nature of these datasets.Knowledge scarcity and temporal lag create knowledge gaps for domain applications.While post-training on domain datasets can embed knowledge into models, existing approaches have some limitations.Continual Pre-Training (CPT) treats all tokens in domain documents with equal importance, failing to prioritize critical knowledge points, while supervised fine-tuning (SFT) with question-answer pairs struggles to develop the coherent knowledge structures necessary for complex reasoning tasks.To address these challenges, we propose Reinforcement Learning from Augmented Generation (RLAG).Our approach iteratively cycles between sampling generations and optimizing the model through calculated rewards, effectively embedding critical and contextually coherent domain knowledge.We select generated outputs with the highest log probabilities as the sampling result, then compute three tailored reward metrics to guide the optimization process.To comprehensively evaluate domain expertise, we assess answer accuracy and the rationality of explanations generated for correctly answered questions.Experimental results across medical, legal, astronomy, and current events datasets demonstrate that our proposed method significantly outperforms baseline approaches.

In prior studies, researchers have proved the excellent performance of various deep neural networks on speaker verification (SV). However, most of the improvements of SV systems are aimed at modifying the specific network structure to enhance its robustness but with limited flexibility. In this paper, MCA-CDD, which is a novel universal residual block module and can easily replace the original residual block without increasing the block number, is proposed with adaptive multi-branch coordinate attention (MCA) and channel-level dynamic difference (CDD). The design of multiple branches enables CA to model time and frequency at multiple scales. In addition, CDD fusion is applied into the feature fusion process of the residual block. The CDD fusion at shallow positions of the model enables the model to learn the detailed speaker-related dynamic texture information of speech. Experiments are conducted on several ResNet backbones and the results on different seen and unseen test sets show significant improvements, outperforming the baseline by about relatively 15-20%.