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Enhancing Low-Resource ASR through Versatile TTS: Bridging the data Gap

Enhancing Low-Resource ASR through Versatile TTS: Bridging t...

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

作者： Guanrou Yang Fan Yu Ziyang Ma Zhihao Du Zhifu Gao Shiliang Zhang Xie Chen MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Shanghai Jiao Tong University China Institute for Intelligent Computing Alibaba Group China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

While automatic speech recognition (ASR) systems have achieved remarkable performance with large-scale datasets, their efficacy remains inadequate in low-resource settings, encompassing dialects, accents, minority languages, and long-tail hotwords, domains with significant practical relevance. With the advent of versatile and powerful text-to-speech (TTS) models, capable of generating speech with human-level naturalness, expressiveness, and diverse speaker profiles, leveraging TTS for ASR data augmentation provides a cost-effective and practical approach to enhancing ASR performance. Comprehensive experiments on an unprecedentedly rich variety of low-resource datasets demonstrate consistent and substantial performance improvements, proving that the proposed method of enhancing low-resource ASR through a versatile TTS model is highly effective and has broad application prospects. Furthermore, we delve deeper into key characteristics of synthesized speech data that contribute to ASR improvement, examining factors such as text diversity, speaker diversity, and the volume of synthesized data, with text diversity being studied for the first time in this work. We hope our findings provide helpful guidance and reference for the practical application of TTS-based data augmentation and push the advancement of low-resource ASR one step further.

关键词： Heavily-tailed distribution Training data Speech enhancement Signal processing Performance gain data augmentation Controllability data models Text to speech Automatic speech recognition

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Anomaly Detection in Meteorological data Using Machine Learning Techniques

Anomaly Detection in Meteorological Data Using Machine Learn...

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Uddhav Rawal Subhas Patel School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore Madhya Pradesh India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Anomaly detection is a very important component in weather forecasting, disaster preparedness and climate modeling in meteorology. We use Density Based Spatial Clustering of Applications with Noise, Isolation Forest, Local Outlier Factor, Elliptic Envelope and One Class Support Vector Machine to detect anomalies in Tunisian meteorological data in this study. Key meteorological parameters, such as wind speed, rainfall and temperature, was analyzed as the important indicators of atmospheric conditions. DBSCAN detected local density changes very well and is therefore a good choice for anomaly detection in areas with complex spatial patterns from the models used. Isolation Forest algorithm was able to find global anomalies with robust performance, giving very high true positive rates with minimum false positives. The use of these machine learning techniques is complementary and this dual approach shows how they can be used to detect different kinds of anomalies. The results show how machine learning can be used to make environmental monitoring and weather prediction much more accurate and reliable, with the ability to detect anomalies precisely and reliably. Having such capabilities is critical for taking proactive actions in response to extreme weather events, and to be able to intervene at the right time to minimize the impacts of the events. Additionally, these models could be integrated into real time monitoring systems to provide additional utility in optimizing their use and provide actionable insight for disaster management and climate resilience. Further research could involve the fusion of these algorithms with deep learning methods, larger datasets, and real time deployment to build intelligent systems that can learn and adapt to changing environmental conditions. This would be a large advance in applying machine learning in meteorology.

关键词： Support vector machines Machine learning algorithms Rain Wind speed Atmospheric modeling Weather forecasting Forestry Real-time systems Tuning Anomaly detection

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Performance Analysis of Different Graphs in Graph-Based Remaining Useful Life Prediction Model

Performance Analysis of Different Graphs in Graph-Based Rema...

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Annual ieee systems conference

作者： Abrar Mahi Al Rashid Anwar Hosen Burhan Khan Institute for Intelligent Systems Research and Innovation Deakin University Geelong Australia

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Condition-based maintenance has progressed substantially through the integration of IoT and advanced sensing technologies. Prognostics and Health Management (PHM) has emerged as a pivotal strategy for enhancing reliability and minimizing downtime by facilitating early fault detection and optimizing maintenance planning. Predicting the Remaining Useful Life (RUL) is a vital component of prognostics and health management for machinery. It effectively prevents catastrophic failures and reduces the costs associated with unplanned maintenance. Numerous Deep Learning (DL) methodologies have been applied for predicting RUL owing to the versatility of their architectures. This paper investigates the use of Graph Neural Networks (GNNs) for RUL estimation. GNNs are adept at modeling complex relationships within equipment data by representing components and their connections as nodes and edges in a graph, thereby capturing intricate dependencies and operational patterns. Building on recent advancements, this study conducts a comparative analysis of various methods for constructing adjacency matrices used in GNNs, crucial for accurate RUL estimation. The study leverages the Graph Attention Model (GAT) to assess the effectiveness of different graph structures in predicting RUL, especially under conditions of machinery degradation. Our findings emphasize the importance of edge quality and quantity in determining the performance of GAT models. This paper concludes with an in-depth discussion on the impact of various graph construction methods on RUL estimation, providing insights for future research and practical applications in industrial settings.

关键词： Degradation Computational modeling Estimation Predictive models Graph neural networks Maintenance Sensors Prognostics and health management Machinery Load modeling

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Sumit Saklani Mahesh Manchanda Raman Sharma Devendra Singh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments.

关键词： Deep learning Accuracy Rain Smart cities Scalability Urban planning Traffic control Real-time systems Safety Internet of Things

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Natural Language Processing for Conversational AI: Chatbots and Virtual Assistants

Natural Language Processing for Conversational AI: Chatbots ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Neeraj Shrivastava Pushpa Tewari S. Sujatha Srinivasa Rao Bogireddy Neeraj Varshney Vinod Sharma Dept. of Engineering Medicaps University Indore India Dept. of Vocational Studies Guru Nanak College Dhanbad India Dept. of Civil Engineering K. Ramakrishnan College of Technology Trichy India Dept. of Software Engineer Horizon Systems Inc Phoenix Arizona Dept. of CSE GLA University Mathura India Dept. of Computer Science Jiwaji University Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The frontline in developing the capabilities of chatbots and virtual assistants involves the use of NLP, enabling these machines to evolve into truly advanced, interactive machines that are able to comprehend and respond to human language with an almost unimaginable degree of accuracy. The paper will analyze some of the major NLP techniques and methodologies used for such systems: machine learning algorithms, neural networks, and semantic analysis. This would also mean that, with the help of big data and continuous learning, the work of chatbots and virtual assistants will range from answering queries to personalized recommendations. Language understanding, generation, and context management are discussed in detail, underlining how NLP improves user experience by making interactions more natural and intuitive. The challenges of ambiguous language processing, conversational context, and ensuring data privacy and security are discussed as well. Case studies and applications in real life show the impact of NLP on different sectors: customer service, healthcare, and e-commerce. The findings bring out the potential of NLP to change human-computer interaction and allow for more responsive and intelligent digital assistants.

关键词： Ethics Technological innovation Sentiment analysis Virtual assistants Semantics Speech recognition Chatbots Natural language processing User experience Text processing

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FPGA-based Real-time High-resolution Image Processing for rocket on-board processing

FPGA-based Real-time High-resolution Image Processing for ro...

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international conference on Computer Research and Development (ICCRD)

作者： Hui Zhou Xiaofeng Wang YuJia Xie Yongxiang Cao Yu Tang Yongchun Huo Huan Wen Xiongbo Zhao Chaoran Li Beijing Aerospace Automatic Control Institute National key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing China Computer Science and Engineering Beihang University Beijing China National Superior College for Engineers Beihang University Beijing China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-the-art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-to-end computation time for

关键词： YOLO Rockets Power demand Convolution Multicore processing Reviews Scalability Accelerator architectures Real-time systems Field programmable gate arrays

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A Feature Transformation Technique for Improving Ensemble Learning systems 17th

A Feature Transformation Technique for Improving Ensemble L...

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17th Asian conference on intelligent Information and database systems, ACIIDS 2025

作者： Nguyen, Truong Thanh Vu, Hieu Dang, Truong Elyan, Eyad Vu, Thanh Son Nguyen, Tien Thanh School of Computing Engineering and Technology RGU Aberdeen United Kingdom School of Information and Communication Technology Thuyloi University Hanoi Viet Nam

ISBN: (纸本)9789819660049

In this study, we propose a feature transformation approach to improve the performance of Ensemble Learning systems. Our method operates on the predictions of base classifiers within an ensemble system, known as meta-data, which are produced by applying a Cross-Validation procedure on the training data using various learning algorithms. The goal is to transform the meta-data to obtain a better combined output. Specifically, we compute the center vectors associated with each class label on the meta-data. We then increase the dimensionality of the meta-data by concatenating it with residuals, which are obtained by subtracting each vector in the meta-data from the associated center vector. A classifier is then trained on this transformed meta-data to create the combiner which will be used to combine outputs of base classifiers for ensemble prediction. Our method is compared with three well-known ensemble methods: deep forest (gcForest), Decision Template (DT), Sum rule, and the same ensemble without transformation. We evaluated the experimental methods on 22 UCI datasets and used the Friedman and Nemenyi tests to statistically compare their performances. The results show that our method outperforms all base classifiers and the benchmark algorithms on experimental datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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IoT-Based Transfer Learning and Genetic Algorithm Approaches for Smart Environmental Pollution Detection and Management

IoT-Based Transfer Learning and Genetic Algorithm Approaches...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： Shafqat Alauddin Sree Vidhya S S. Pragadeeswaran P. Sowjanya Rajashree Bhokare Maaz Allah Khan Department of Chemistry Shibli National College Azamgar Uttar Pradesh India Department of Computer Science and Engineering Erode Sengunthar Engineering College Thudupathi Tamilnadu India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Malla Reddy Engineering College (Autonomous) Dhulapally Telangana India Department of Electrical Engineering Dr. D. Y. Patil Institute of Technology Pimpri Pune Maharashtra India Department of Engineering &Technology Isabella Thoburn College (Professional Studies) Lucknow Uttar Pradesh India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The identification of environmental pollution remains to be a significant issue, as conventional technologies lack scalability and flexibility to dynamic, real-time situations. This study presents an IoT-enabled hybrid model that combines transfer learning and genetic algorithms to overcome these restrictions. IoT networks enable real-time, extensive data acquisition, whereas transfer learning uses pre-trained models for effective feature extraction and domain adaption. Genetic algorithms enhance hyperparameters and sensor configurations, guaranteeing energy economy and optimal model performance. The suggested CNN-LSTM architecture utilising multi-scale attention attains a training accuracy of 95.6% and an F1 score of 0.96, exceeding the performance of conventional Random Forest and CNN models in accuracy, recall, and precision. The comparative study confirms the model's strength and flexibility, illustrating its capacity for scaled, practical applications. This framework creates a dependable and effective system for intelligent environmental pollution control, providing substantial improvements in detection accuracy and operational efficacy compared to current methods.

关键词： Training Adaptation models Pollution Accuracy Scalability Transfer learning Real-time systems Internet of Things Random forests Genetic algorithms

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IFKG: An intelligent Fault Diagnosis Tool with Knowledge Graph and Generative LLM

IFKG: An Intelligent Fault Diagnosis Tool with Knowledge Gra...

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ieee international conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Xixuan Yang Tong Jia Ying Li Gang Huang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

The development of effective diagnostic methodolo-gies for software system failures is of paramount importance. Traditional methods, which rely on specialized terminology and intricate reasoning, require users to have a technical background, resulting in reduced flexibility and decreased user-friendliness. With the rise of generative large language models, optimizing human-computer interaction has become a critical area of focus. Additionally, the inherent intelligence and extensive knowledge of large language models make them both easy and effective to employ for fault diagnosis assistance. We introduce IFKG, an advanced tool for diagnosing software system failures. IFKG integrates generative large language models with knowledge graphs, employing natural language interactions to implement fault detection and deliver solutions. IFKG enables users to upload descriptive problems, retrieve pertinent information from the knowledge graph, and present diagnostic results in natural language. Our accuracy assessments across diverse software system failures indicate that the IFKG provides targeted and actionable recommendations, effectively assisting users in ad-dressing a range of software system issues. The tool is available on GitHub at https://***/mako-xxlIFKG, and the demo video can be found on YouTube: https://***2vgZm2hk.

关键词： Fault diagnosis Human computer interaction Video on demand Terminology Large language models Natural languages Knowledge graphs Software systems Web sites Software development management

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Enhancing IoT ECO System - Smart Parking System

Enhancing IoT ECO System - Smart Parking System

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Avinash S Muthukrishnan S R. Aishwarya Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The Smart Parking System is an innovative solution designed to tackle the challenges of parking in densely populated urban areas. By integrating advanced technologies such as RFID (Radio Frequency Identification) sensors, automated gate systems, and proximity sensors, the system delivers a streamlined and efficient parking experience. RFID technology automates entry and exit processes by recognizing vehicles equipped with RFID tags, reducing wait times and enhancing security through controlled access. Proximity sensors at each parking spot monitor occupancy in real-time, feeding data to a central management system that directs drivers to available spaces via digital signage or a mobile app. This real-time guidance decreases search time, reduces congestion, and improves overall parking organization. Designed with scalability and future-readiness, the system's central management unit can integrate additional features like payment processing, reservation systems, and predictive analytics powered by machine learning. This adaptability ensures the system remains relevant as technology advances and user needs evolve. The Smart Parking System also promotes sustainability by minimizing vehicle idling and carbon emissions, contributing to a reduced environmental impact. For users, the system offers a more convenient, secure, and efficient parking experience. For operators, it enhances operational efficiency, reduces manual labor, and provides valuable insights into parking patterns, aiding in pricing, expansion, and maintenance decisions. The system's modular design allows for cost-effective implementation, making it suitable for various facilities from small lots to large complexes. In summary, the Smart Parking System represents a forward-thinking approach to urban parking, combining technology, efficiency, and sustainability to improve urban living.

关键词： Technological innovation Urban areas Automated parking Sensor systems Real-time systems Security Sustainable development intelligent sensors Vehicles Testing

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