The statistical pyramid dense time-delay neural network (SPD-TDNN) model makes it difficult to deal with the imbalance of training data, poses a high risk of overfitting, and has weak generalization ability. To solve these problems, we propose a method based on the joint loss function and improved statistical pyramid dense time-delay neural network (JLF-ISPD-TDNN), which improves on the SPD-TDNN model and uses the joint loss function method to combine the advantages of the cross-entropy loss function and the comparative learning of the loss function. By minimizing the distance between speech embeddings from the same speaker and maximizing the distance between speech embeddings from different speakers, the model could achieve enhanced generalization performance and more robust speaker feature representation. We evaluated the proposed method’s performance using the evaluation indexes of the equal error rate (EER) and minimum cost function (minDCF). The experimental results show that the EEE and minDCF on the Aishell-1 dataset reached 1.02% and 0.1221%, respectively. Therefore, using the joint loss function in the improved SPD-TDNN model can significantly enhance the model’s speaker recognition performance.

Federated learning has been widely applied in automatic speech recognition. However, variations in speaker behaviors result in a significant data imbalance across client devices. Conventional federated speech recognition algorithms typically use fixed probabilities to select clients for each round in model training, often overlooking the disparities in data volume among clients. In reality, the substantial differences in data quantity can extend the training duration and compromise the stability of the global model. Moreover, models trained through federated learning on global data often fail to achieve optimal performance for individual local clients. While personalized federated learning strategies hold promise for enhancing model performance, the inherent diversity of speech data makes it challenging to apply state-of-the-art personalized methods effectively to speech recognition tasks. In this paper, a dynamic client selection algorithm is proposed to solve the problem of data disparities among different clients. It can be effectively combined with most federated learning algorithms and dynamically adjusts the selection probabilities of clients based on their dataset size during training. Experimental results demonstrate that this algorithm saved training time by 26% compared to traditional methods on public datasets while maintaining the equivalent model performance. To optimize the personalized federated learning, this paper proposes a novel group-balanced personalization strategy that fine-tunes groupings of clients based on their dataset size. The experimental results show that this algorithm brought a relatively 12% reduction in character error rate, while it did not increase computational costs. In particular, the group-balanced personalization effectively improved the model performance for clients with smaller datasets than local fine-tuning. The combination of dynamic client selection and group-balanced personalization significantly enhanced training efficiency and model performance.

In deep learning-based speaker verification, the loss function plays a crucial role. Most systems rely on a single loss function, or simply sum multiple losses with manually adjusted weights, increasing experimental complexity and failing to fully leverage the complementary characteristics of different losses. To address this, this paper proposes a speaker verification system based on channel attention and adaptive joint loss optimization. An adaptive joint loss function dynamically adjusts loss weights, allowing the model to better learn the similarities and differences of speakers, narrowing the gap between closed- and open-set testing, and enhancing generalization ability. A channel attention squeeze-and-excitation module is designed to improve the network’s ability to extract channel-specific features. On the AISHELL-1 dataset, the system achieved an equal error rate of 0.84% and a minimum detection cost function of 0.0528. Experimental results demonstrate a significant improvement in speaker verification performance, confirming the effectiveness of the proposed system.

In this paper, we propose an adversarial transfer learning method to solve the lack of data resources for named entity recognition (NER) tasks in spoken language understanding. In the framework, we use bi-directional long short-term memory with self-attention and conditional random field (BiLSTM-Attention-CRF) model which combines character and word information as the baseline model to train source domain and target domain corpus jointly. Shared features between domains are extracted by a shared feature extractor. This paper uses two different sharing patterns simultaneously: full sharing mode and private sharing mode. On this basis, an adversarial discriminator is added to the shared feature extractor to simulate generative adversarial networks (GAN) and eliminate domain-dependent features. This paper compares ordinary adversarial discriminator (OAD) and generalized resource-adversarial discriminator (GRAD) through experiments. The experimental results show that the transfer effect of GRAD is better than other methods. The F1 score reaches 92.99% at the highest, with a relative increase of 12.89%. It can effectively improve the performance of NER tasks in resource shortage fields and solve the problem of negative transfer.

Legal judgment prediction (LJP) is a fundamental task in legal artificial intelligence. LJP is used to predict relevant law articles, charges, and terms of penalty on the basis of the factual description of legal cases. Existing approaches fail to make full use of external legal knowledge, and rarely pay attention to judges who refer to historical similar cases before they make decisions in real judicial scenarios. To simulate this process, this study proposes a dual-stage framework that integrates law article semantics and similar case retrieval (LAS-SCR). This framework generates initial predictions by deeply fusing case facts with law article semantics through cross-modal attention and hierarchical feature augmentation and, targeting easily confused charges identified from the initial output, it corrects these predictions using contrastive learning-based similar case retrieval. Experiments on two real-world legal datasets demonstrate that our method achieves better results than several baseline models do. Furthermore, our method is effective at distinguishing easily confused charges.