Time series forecasting is crucial, yet the challenge of escalating errors in chaotic data and natural phenomena prediction endures. Existing methods for recursive strategies face difficulties in Multi-Input Multi-Output scenarios. A unified learning framework addressing error growth alongside these models is lacking, despite advanced neural networks. While dynamical system theory has inspired research in time series forecasting, these approaches struggle to estimate and mitigate error growth adequately. To address these gaps, we introduce Phase-Space-Guided Forecasting (PSGF), rooted in dynamical system theory. PSGF transforms data into high-dimensional phase space, quantifies error growth rates, and incorporates them into the neural network via Error Growth Awareness Loss (EGAL). PSGF enhances the utilization of dynamical constraints, reducing the need for additional feature engineering or hyperparameter tuning. Experimental results on chaotic systems and real-world climate data demonstrate PSGF’s significant accuracy improvements on diverse deep learning models.

The detection of spoofing speech generated by unseen algorithms remains an unresolved challenge. One reason for the lack of generalization ability is that traditional detecting systems follow the binary classification paradigm, which inherently assumes the possession of prior knowledge of spoofing speech. One-class methods attempt to learn the distribution of bonafide speech and are inherently suited to the task where spoofing speech exhibits significant differences. However, training a one-class system using only bonafide speech is challenging. In this paper, we introduce a teacher-student framework to provide guidance for the training of a one-class model. The proposed one-class knowledge distillation method outperforms other state-of-the-art methods on the ASVspoof 21DF and InTheWild datasets, demonstrating its superior generalization ability.

The wav2vec 2.0 and integrated spectro-temporal graph attention network (AASIST) based countermeasure achieves great performance in speech anti-spoofing. However, current spoof speech detection systems have fixed training and evaluation durations, while the performance degrades significantly during short utterance evaluation. To solve this problem, AASIST can be improved to AASIST2 by modifying the residual blocks to Res2Net blocks. The modified Res2Net blocks can extract multi-scale features and improve the detection performance for speech of different durations, thus improving the short utterance evaluation performance. On the other hand, adaptive large margin fine-tuning (ALMFT) has achieved performance improvement in short utterance speaker verification. Therefore, we apply Dynamic Chunk Size (DCS) and ALMFT training strategies in speech anti-spoofing to further improve the performance of short utterance evaluation. Experiments demonstrate that the proposed AASIST2 improves the performance of short utterance evaluation while maintaining the performance of regular evaluation on different datasets.

Wave data mining and processing are important in ocean prediction. However, wave data often exhibits imbalance, resulting in low accuracy in predicting extreme phenomena. To alleviate this problem, we propose a hybrid feature selection method based on imbalance learning (HFS-IL) to improve accuracy of prediction models. Specifically, we first use a Long Short-Term Memory (LSTM) network to train an imbalance discriminator, which aims to classify input data into common and rare subsets. Secondly, we select the optimal feature subsets by a hybrid feature selection algorithm, which is innovatively designed by combining mutual information and forward selection. To verify the effectiveness of HFS-IL, we process an imbalanced wave dataset from ERA5 by HFS-IL and use the processed data as input for an intelligent prediction model. The experimental results demonstrate that HFS-IL can effectively alleviate the impact of data imbalance and improve the accuracy of prediction, especially at station 51000, the majority of metrics outperform GRU and OSP-FEAN.

Current synthetic speech detection (SSD) methods perform well on specific datasets but require improvement in interpretability and robustness. One possible reason is the lack of interpretability analysis of synthetic speech defects. In this paper, the flaws in the temporal consistency (TC) of speaker features inherent in the speech synthesis process are analyzed. Differences in the TC of intra-utterance speaker features arise due to limited control over speaker features during speech synthesis. The speech generated by text-to-speech algorithms exhibits higher TC, while the speech generated by voice conversion algorithms yeilds slightly lower TC compared to bonafide speech. Based on this finding, a new SSD method based on the TC of speaker features is proposed. Modeling the TC of intra-utterance speaker features extracted by a pre-trained ASV system can be used for SSD. The proposed method achieves equal error rates of 0.84%, 3.93%, 12.98% and 24.66% on the ASVspoof 2019 LA, 2021 LA, 2021 DF and IntheWild evaluation datasets, respectively, demonstrating strong interpretability and robustness.

The current speech anti-spoofing countermeasures (CMs) show excellent performance on specific datasets. However, removing the silence of test speech through Voice Activity Detection (VAD) can severely degrade performance. In this paper, the impact of silence on speech anti-spoofing is analyzed. First, the reasons for the impact are explored, including the proportion of silence duration and the content of silence. The proportion of silence duration in spoof speech generated by text-to-speech (TTS) algorithms is lower than that in bonafide speech. And the content of silence generated by different waveform generators varies compared to bonafide speech. Then the impact of silence on model prediction is explored. Even after retraining, the spoof speech generated by neural network based end-to-end TTS algorithms suffers a significant rise in error rates when the silence is removed. To demonstrate the reasons for the impact of silence on CMs, the attention distribution of a CM is visualized through class activation mapping (CAM). Furthermore, the implementation and analysis of the experiments masking silence or non-silence demonstrates the significance of the proportion of silence duration for detecting TTS and the importance of silence content for detecting voice conversion (VC). Based on the experimental results, improving the robustness of CMs against unknown spoofing attacks by masking silence is also proposed. Finally, the attacks on anti-spoofing CMs through concatenating silence, and the mitigation of VAD and silence attack through low-pass filtering are introduced.