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Elevating Robust ASR By Decoupling Multi-Channel Speaker Separation and Speech Recognition

Elevating Robust ASR By Decoupling Multi-Channel Speaker Sep...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yufeng Yang Hassan Taherian Vahid Ahmadi Kalkhorani DeLiang Wang Department of Computer Science and Engineering The Ohio State University USA Center for Cognitive and Brain Sciences The Ohio State University USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Despite the tremendous success of automatic speech recognition (ASR) with the introduction of deep learning, its performance is still unsatisfactory in many real-world multi-talker scenarios. Speaker separation excels in separating individual talkers but, as a frontend, it introduces processing artifacts that degrade the ASR backend trained on clean speech. As a result, mainstream robust ASR systems train on noisy speech to avoid processing artifacts. In this work, we propose to decouple the training of the multi-channel speaker separation frontend and the ASR backend, with the latter trained only on clean speech. On SMS-WSJ, the proposed approach achieves a word error rate (WER) of 5.74%, outperforming the previous best by 14.3%. Furthermore, on recorded LibriCSS, we achieve the speaker-attributed WER of 3.86%, outperforming the previous best system trained on the same data by 24.8%. These state-of-the-art results suggest that decoupling speech separation and recognition is a potentially effective approach to robust ASR.

关键词： Training Deep learning Error analysis Signal processing Acoustics Noise measurement Speech processing Automatic speech recognition

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international conference on Information Networking

作者： Yejung Lee Yohan Park Hyston Kayange Jihwan Um Jongsun Choi Jaeyoung Choi School of Computer Science and Engineering Soongsil University Seoul South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

The accumulation of large-scale data in recommendation systems can significantly increase resource consumption during model training. In particular, when redundant data exists, the model may overfit as it evaluates test data based on already seen, similar data during the training process. To address the issues of resource consumption and overfitting, this study proposes a lightweight similarity-based algorithm to reduce interaction data. The proposed algorithm decomposes interaction data using matrix factorization to derive a user matrix and then calculates the similarity between users using cosine similarity. The data is then reduced by removing the data of users with fewer items among pairs of users whose similarity exceeds a certain threshold. Experiments were conducted to analyze the impact of the proposed algorithm on recommendation performance and training time after data reduction. Furthermore, the algorithm’s effectiveness was evaluated through performance comparisons with existing data reduction methods. The primary contribution of this study is the introduction of a lightweight similarity-based approach that focuses on eliminating redundant user data in recommendation systems, thereby preventing overfitting and minimizing resource consumption.

关键词： Training Degradation Prevention and mitigation Data models Matrix decomposition Recommender systems Overfitting

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Hygiene Guardian: Proximity - Activated Sanitizing Dustbin with AI Vision

Hygiene Guardian: Proximity - Activated Sanitizing Dustbin w...

引用

2025 international conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Anand, K. Aathmihan, S.B. Akshithaa, B.K. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527495

In metropolitan settings, efficient trash disposal is essential because of growing public health concerns and the potential for disease spread from improperly handled rubbish. By combining IoT and image recognition technology, the Hygiene Guardian provides a clever solution. Human identification is made possible using an AI model that was trained utilizing Edge Impulse at the system's heart. Contactless disposal is made possible by the dustbin's lid opening automatically when a human is recognized. By preventing animals from reaching the bin and lowering hygiene concerns, this minimizes contamination and garbage dispersal. The device has an automatic sanitation option to improve cleanliness even further. After each use, a pump motor sprays disinfectant inside and outside the bin to reduce the growth of dangerous bacteria and viruses. In public areas with heavy traffic, when hand cleaning is difficult, this is especially advantageous. The Hygiene Guardian is an example of how automation and the Internet of Things may enhance public sanitation and offer a scalable solution for cleaner urban settings. © 2025 IEEE.

关键词： Sanitation

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Quality Evaluation of Ideological and Political Education for College Students based on Artificial Neural Networks

Quality Evaluation of Ideological and Political Education fo...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Xue Peng Yan Han School of Marxism Chengdu Polytechnic Chengdu China Department of Science and Research Chengdu Polytechnic Chengdu China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

From past few years, assessing and evaluating quality of college students Ideological and Political Education (IPE) played a vital role by enabling educators to pinpoint areas for improvement and gauge the effectiveness of education by enhancing student learning outcomes. Traditional approaches for evaluating political and ideological education had faced several challenges which include inability to handle complex data and lack of real-time assessment. Therefore, this research proposes Artificial Neural Networks (ANN) for evaluating political and ideological education. Initially, evaluation system is developed for IPE by integrating multitask learning and softmax regression for ensuring accurate assessments. Then, ANN is designed for evaluating IPE by learning complex relationships between input variables and output predictions. These ANN gave comprehensive evaluation output based on various criterions like educational effectiveness, campus style, and social reputation by providing robust assessment of IPE. The proposed ANN attained better results in terms of Mean Percentage Error Ratio MPER (90%) when compared with existing Radial Basis Function Neural Network (RBFNN).

关键词： Accuracy Input variables Education Artificial neural networks Radial basis function networks Learning (artificial intelligence) computer architecture Real-time systems Blockchains Intelligent systems

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Comparative Analysis of Stock Price Forecasting using MTF Image Transformation: Evaluation of Random Forest and CNN Models

Comparative Analysis of Stock Price Forecasting using MTF Im...

引用

Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Vipin Kumar Pandey Tanveer Ahmed Gopinath Sahoo Department of Mathematics Bennett University Greater Noida India Department of Computer Science Bennett University Greater Noida India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Accurate stock price forecasting is a pivotal endeavor in financial markets, essential for informed investment decisions and effective economic strategies. Traditional models such as CAPM, Black-Scholes, ARIMA, and GARCH often struggle to capture the complex, non-linear dynamics inherent in market data. This study presents a comparative analysis of deep learning-based approaches to predict trends in the financial time series. We propose a novel methodology that transforms raw financial data into image representations using the Markov Transformation Field (MTF) alongside a specialized image creation algorithm tailored for financial analysis. These images are then classified into the Buy, Sell, and Hold categories. The approach is evaluated on a dataset comprising twenty stocks from the Indian financial sector, offering a comprehensive assessment of model performance on a substantial scale. To be precise, our experiments suggest an F1 score of 73%. These numbers demonstrate that the proposed deep learning-based technique achieves promising accuracy, highlighting its potential advantages over traditional forecasting methods in capturing the behavior of financial markets.

关键词： Economics Accuracy Biological system modeling Computational modeling Time series analysis Neural networks Predictive models Market research Numerical models Forecasting

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Detection of False Data Injection Attacks in Power Grids Based on Spatiotemporal Feature Fusion

Detection of False Data Injection Attacks in Power Grids Bas...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Bin Li School of Computer Science Northeastern Electric Power University Jilin China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

With the development of new power systems, fast and accurate detection of False Data Injection Attacks (FDIA) is crucial for the secure operation of power grids. Existing FDIA detection models based on spatiotemporal correlations have poor feature extraction capabilities and suffer from feature shift, facing the issue of disrupted inherent spatiotemporal correlations in measurement data. To address this, we propose a detection model based on adaptive fusion of spatiotemporal features. First, Graph Convolutional Networks (GCN) is used to extract static spatial features from the power grid topology, while Graph Attention Networks (GAT) captures the dynamic spatial features. Next, Long Short-Term Memory (LSTM) is employed to analyze the temporal variations and extract temporal features. Finally, the extracted spatiotemporal information is projected into feature space for alignment, and feature fusion is performed using a bilinear attention mechanism. The fused features are then used for FDIA detection. Experimental results show that, compared to existing detection models, the proposed model performs better in terms of detection accuracy and robustness.

关键词： Adaptation models Correlation Accuracy Power system dynamics Feature extraction Data models Robustness Spatiotemporal phenomena Topology Long short term memory

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Peculiarities of Wavelet Transform Application for EEG Signal Analysis During Vibration Influence

Peculiarities of Wavelet Transform Application for EEG Signa...

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Digital Signal Processing and its Applications (DSPA), international conference on

作者： Tatyana N. Matveeva Andrey N. Brysin Alexandra V. Torchinskaya Daniel G. Kornilov Egor I. Spivak School No. 6: Aerospace Moscow Aviation Institute (National Research University) (MAI Moscow Aviation Institute) Moscow Russia CS 1 «Data protection» MIREA - Russian Technological University (RTU MIREA Moscow Russia School No. 3: Control Systems Computer Science and Power Engineering Moscow Aviation Institute (National Research University) (MAI Moscow Aviation Institute) Moscow Russia «RZD-TECHSERVICE» LLC Moscow Russia «BOKSMETR» LLC Moscow Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper deals with the reaction of the vehicle operator's organism to external vibration and the development of a monitoring system at the early stages of vibration effects. The choice of wavelet transform as the main method of signal processing is justified. The zones of organism reaction to external overloads are singled out, and the application of Gaussian mother wavelets for electroencephalogram analysis, except Morlet wavelet, at vibration impact on the vehicle operator is evaluated. Experimental work was carried out on the EGV 10–100 stand of the vibromechanics laboratory of IMASH RAS.

关键词： Wavelet transforms Vibrations Digital signal processing Wavelet analysis Organisms Electroencephalography Signal analysis Monitoring

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DGIResNet-based multi-gene classification method for adult diffuse glioma using MRI 25

DGIResNet-based multi-gene classification method for adult d...

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Proceedings of the 2025 5th international conference on Bioinformatics and Intelligent Computing

作者： Hongwei Zhang Shaoguo Cui Liqiang Zhang Junshan Chen Yongmei Li College of Computer and Information Science Chongqing Normal University Chongqing China The First Afliated Hospital of Chongqing Medical University Chongqing China

ISBN: (纸本)9798400712203

Predicting the histological grading and genetic biomarkers of gliomas using medical imaging and deep learning is a highly challenging task with significant importance for providing personalized treatment and survival prediction. Previous studies have primarily focused on predicting the status of a single or two genes. This paper firstly proposes a new end-to-end network framework, GIResNet, to simultaneously predict the status of five genotypes. Our model, based on the ResNet architecture, optimizes channel and spatial scales by concatenating T1C and T2F images. Compared to the original ResNet, our approach demonstrates higher lesion attention and improvements in various metrics, showcasing strong potential for practical applications.

关键词： Adult diffuse glioma

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Brain-computer Interface Framework for Real-Time Application Adaptation

Brain-Computer Interface Framework for Real-Time Applicatio...

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

作者： Dzikiewicz, Pawel Koralewski, Tomasz Ner, Jakub Pawlowski, Adam Kedziora, Michal Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9789819660070

Adaptive real-time applications that respond to users’ cognitive and emotional states have emerged as a critical research frontier in brain-computer interface (BCI) technology. However, existing solutions often fail to provide a cohesive toolkit that seamlessly bridges EEG signal processing, cognitive state detection, and practical software adaptation. To address this gap, we developed a structured research methodology involving EEG data collection, signal preprocessing, multi-state classification, and iterative model refinement. In controlled experiments, participants performed tasks specifically designed to induce boredom, flow, and frustration;the resulting EEG data provided the foundation for training machine learning models that could accurately recognize these states in real time. Based on these findings, we propose a flexible and modular BCI framework for Unity-based environments. This framework unites robust EEG-driven state detection with adaptive software design and was validated through two proof-of-concept applications: a game featuring dynamic difficulty adjustment and a vigilance support system for high-stakes professions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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Advancing Brain Tumor Detection in MRI Images Using Convolutional Neural Networks and Deep Learning Techniques

Advancing Brain Tumor Detection in MRI Images Using Convolut...

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Disruptive Technologies (ICDT), international conference on

作者： Gungun Khatana Praveen Kumar Rai Shweta Rini Dabral Department of Computer Science and Engineering GL Bajaj Institute Technology and Management Greater Noida

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Brain cancer remains a major health challenge, particularly in constraint-driven settings like India, where it is the 10th leading cause of cancer-related deaths. Early detection is vital to improving treatment outcomes, yet traditional methods relying on manual MRI interpretation can be time taking, expensive, and highly susceptible to error. This study is presenting a deep learning based approach which is using Convolutional Neural Networks (CNNs). The proposed system uses advanced data preprocessing techniques, including Z-score normalization, skull stripping, and augmentation, to optimize MRI datasets. A robust CNN model was developed and trained on a diverse dataset comprising glioma, meningioma, pituitary, and non-tumor cases. This model achieved exceptional performance, with training, validation, and test accuracies of 99.95%, 99.8%, and 99.3%, respectively, along with high accuracy, recall, and F1 points. Evaluation metrics such as ROC-AUC and confusion matrix analysis showcased the model's accuracy in differentiating tumour types and stages.

关键词： Deep learning Training Measurement Accuracy Magnetic resonance imaging Data preprocessing Manuals Brain modeling Convolutional neural networks Disruptive technologies

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