Kejing Xiao

Graph neural networks (GNNs) have emerged as powerful tools for news text classification by explicitly modeling inter-document relationships and complex semantic dependencies. This paper presents the first comprehensive survey specifically examining GNN methods tailored for news text classification, addressing the unique challenges that distinguish news data from general text: temporal dynamics of rapidly evolving events, multi-dimensional complexity across political–economic–social aspects, dense entity networks, and credibility verification requirements. The authors systematically analyze GNN-based approaches from three core dimensions: (1) graph construction methods, from word co-occurrence and syntactic–semantic relationships to news-specific temporal-aware and event-centric graphs; (2) feature extraction mechanisms leveraging both node and edge characteristics; and (3) four major GNN architectures—GCN, GAT, GGNN, and GTN—evaluating their specific advantages for news classification tasks. The survey identifies critical challenges including computational scalability for real-time news streams, dynamic graph adaptation, multi-modal integration, and interpretability requirements. This comprehensive review provides researchers and practitioners with a systematic understanding of how GNN architectures can be adapted to address the distinctive requirements of news text classification, establishing a foundation for future developments in this rapidly evolving field.

Text classification is an important task in the field of natural language processing, aiming to automatically assign text data to predefined categories. The BertGCN model combines the advantages from both BERT and GCN, enabling it to effectively handle text data for classification. However, there are still some limitations when it comes to handling complex text classification tasks. BERT processes sequence information in segments and cannot directly capture long-distance dependencies across segments, which is a limitation when dealing with long sequences. GCN tends to suffer from over-smoothing problem in deep networks, leading to information loss. To overcome these limitations, we propose the XLG-Net model, which integrates XLNet and GCNII to enhance text classification performance. XLNet employs permutation language modeling and improvements of the Transformer-XL architecture, not only improving the ability to capture long-distance dependencies but also enhancing the model’s understanding of complex language structures. Additionally, we introduce GCNII to overcome the over-smoothing problem in GCN. GCNII effectively retains the initial features of nodes by incorporating initial residual connections and identity mapping mechanisms, ensuring effective information transmission even in deep networks. Furthermore, to achieve excellent performance on both long and short texts, we apply the design philosophy of DoubleMix to the XLNet model, using a hybrid approach of mixing hidden states improves the model’s accuracy and robustness. Experimental results demonstrate that the XLG-Net model achieves significant performance improvements on four benchmark text classification datasets, validating the model’s effectiveness on complex text classification tasks.

Dysarthric speech has several pathological characteristics, such as discontinuous pronunciation, uncontrolled volume, slow speech, explosive pronunciation, improper pauses, excessive nasal sounds, and air-flow noise during pronunciation, which differ from healthy speech. Automatic speech recognition (ASR) can be very helpful for speakers with dysarthria. Our research aims to provide a scoping review of ASR for dysarthric speech, covering papers in this field from 1990 to 2022. Our survey found that the development of research studies about the acoustic features and acoustic models of dysarthric speech is nearly synchronous. During the 2010s, deep learning technologies were widely applied to improve the performance of ASR systems. In the era of deep learning, many advanced methods (such as convolutional neural networks, deep neural networks, and recurrent neural networks) are being applied to design acoustic models and lexical and language models for dysarthric-speech-recognition tasks. Deep learning methods are also used to extract acoustic features from dysarthric speech. Additionally, this scoping review found that speaker-dependent problems seriously limit the generalization applicability of the acoustic model. The scarce available speech data cannot satisfy the amount required to train models using big data.

Electro-laryngeal (EL) speech has poor intelligibility and naturalness, which hampers the popular use of the electro-larynx. Voice conversion (VC) can enhance EL speech. However, if the EL speech to be enhanced is with complicated tone variation rules in Mandarin, the enhancement will be less effective. This is because the source speech (Mandarin EL speech) and the target speech (normal speech) are not strictly parallel. We propose using cycle-consistent generative adversarial networks (CycleGAN, a parallel-free VC framework) to enhance continuous Mandarin EL speech, which can solve the above problem. In the proposed framework, the generator is designed based on the neural networks of a 2D-Conformer-1D-Transformer-2D-Conformer. Then, we used Mel-Spectrogram instead of traditional acoustic features (fundamental frequency, Mel-Cepstrum parameters and aperiodicity parameters). At last, we converted the enhanced Mel-Spectrogram into waveform signals using WaveNet. We undertook both subjective and objective tests to evaluate the proposed approach. Compared with traditional approaches to enhance continuous Mandarin EL speech with variable tone (the average tone accuracy being 71.59% and average word error rate being 10.85%), our framework increases the average tone accuracy by 12.12% and reduces the average errors of word perception by 9.15%. Compared with the approaches towards continuous Mandarin EL speech with fixed tone (the average tone accuracy being 29.89% and the average word error rate being 10.74%), our framework increases the average tone accuracy by 42.38% and reduces the average errors of word perception by 8.59%. Our proposed framework can effectively address the problem that the source and target speech are not strictly parallel. The intelligibility and naturalness of Mandarin EL speech have been further improved.