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Speech Recognition Paradigms: A Comparative Evaluation of SpeechBrain, Whisper and Wav2Vec2 Models

Speech Recognition Paradigms: A Comparative Evaluation of Sp...

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International Conference for Convergence of Technology (I2CT)

作者： Dhanvanth Reddy Yerramreddy Jayasurya Marasani Ponnuru Sathwik Venkata Gowtham Guduru Harshit Anjali Dept. of Computer Science and Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Amritapuri India Dept. of Electronics & Communication Engineering School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Speech recognition plays a pivotal role in the realm of natural language processing that deals in converting the language into the written text, providing human-computer interaction and enables us to use it widely for applications starting with voice assistants and delving upto the transcription services. Due to the complexity present in performing the task of speech recognition has led to the development of various models to enhance accuracy and efficiency. Our project mainly delves into three prominent speech recognition models that are Whisper, Wav2Vec2 and Speechbrain each of them representing distinct approaches of transcribing spoken language. The significance of the models used lies in their potential to perform better for realtime applications by improving the accuracy and reliability of speech recognition. To evaluate the best model that performs effectively, an array of metrics are used including levenshtein distance and it’s similarity percentage, jaccard similarity along with semantic similarity that provides an additional layer of evaluation describing the model’s understanding of contextual meaning in spoken language. Out of the three models, Speech Brain model outperformed all the other models through the calculation of Word Error Rate (WER), Character Error rate (CER) and BLEU score. The results have shown the model’s efficiency in converting spoken language(audio files) into precise and contextually relevant text. The results shows that these models contribute to the field of speech recognition highlighting the strengths of each approach and among them considering speechbrain as an ideal solution for accurate and meaningful transcription.

关键词： Measurement Human computer interaction Adaptation models Technological innovation Error analysis Semantics Speech recognition

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Automatic Concrete Surface Crack Recognition Using EfficientNetV2 Variants

Automatic Concrete Surface Crack Recognition Using Efficient...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Rama, A. Damasevicius, Robertas Arunmozhi, S. Mohammed, Mazin Abed Hussam, Ragheed Rajinikanth, V. Saveetha School of Engineering Simats Department of CSE TN Chennai602105 India Kaunas University of Technology Centre of Real Time Computer Systems Kaunas51423 Lithuania Manakula Vinayagar Institute of Technology Department of Electronics and Communication Engineering TN Puducherry605 107 India College of Computer Science and Information Technology University of Anbar Department of Artificial Intelligence Anbar31001 Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad10021 Iraq Saveetha School of Engineering Simats Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India

ISBN: (纸本)9798350315127

Building health and structural integrity are important to ensure occupant safety and long-term durability. Monitoring and assessing building condition regularly ensures that potential problems are identified early. This research aims to develop and implement a deep-learning (DL) supported tool to examine the concrete surface images collected using digital camera. The data gathering forms the first stage and the chosen DL algorithm is used for feature extraction followed by the feature selection and then fusion is done and finally a fivefold cross verification schema is applied for a binary classification. This work considered the EfficientNetV2 (ENV2) model with variants like small, medium and large for the examination. The investigation is implemented using individual and fused deep-features and the performance of the developed tool is confirmed using the achieved accuracy. The outcome of this research confirms that the proposed tool produces a betterment of >96% accuracy with individual-features and 100% accuracy with fused-features. These results confirm that the proposed scheme provides significant result during the crack detection. © 2023 IEEE.

关键词： Crack detection

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IoT ML Driven Holistic Health Monitoring and Fitness Assessment Empowering Proactive Wellbeing Management

IoT ML Driven Holistic Health Monitoring and Fitness Assessm...

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Students Conference on engineering and Systems (SCES)

作者： Sachin Mishra Anurag Singh Dept. of Computer Science and Engineering Government Engineering College Raipur Raipur Chhattisgarh India Dept. of Electronics & Communication Engineering International Institute of Information Technology Chhattisgarh Naya Raipur India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374728

In recent years, significant advancements in the area of non-invasive and wearable technology have paved the way for widespread applications of personalized and remote health monitoring. Health monitoring enables proactive management of one's well-being, and enhances overall health outcomes. With the advent of such technologies there are lots of platforms developed. But such platforms are not successful in providing the followings: 1) the ability to monitor all major health vitals in one unified application, & 2) the provision of a holistic health score derived from these non-invasive vitals for fitness measurement. In response, this paper presents a novel solution featuring: 1) An IoT-ML enabled platform for self-monitoring of important health vitals, and 2) A fitness assessment score derived through ML algorithm using the health vitals of a person. By integrating data on various health parameters including heart rate, blood oxygen levels, and sleep patterns, advanced machine learning models are developed to analyze this data and predict the fitness score that accurately reflects an individual's overall health and fitness level. The performance of the proposed estimation model is evaluated using R2 and mean square error (MSE) and the best value is achieved using the XGBoost regressor of values 0.967 and 0.0033. This solution empowers individuals to take control of their well-being and improve their overall quality of life. Overall, the integration of non-invasive methods and IoT sensors for health vitals monitoring and fitness assessment presents a novel approach with significant potential to revolutionize health care. By empowering individuals with real-time insights and a comprehensive fitness score, this solution facilitates informed decision-making and enables individuals to proactively manage their health and well-being, ultimately enhancing their overall quality of life.

关键词： Measurement Pediatrics Medical services Mean square error methods Predictive models Prediction algorithms Real-time systems

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Optimization and Exploration of the Volume Scheduling Problem in Logistics Network

Optimization and Exploration of the Volume Scheduling Proble...

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Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： Junyou Luo Xinyue Gong Hao Tan Chunguang Chen Yu Kang Taoran Fu School of Computational Science and Electronics Hunan Institute of Engineering Xiangtan China School of Computer and Communication Hunan Institute of Engineering Xiangtan China

Compared to traditional business activities, e-commerce activities are more susceptible to the impact of logistics networks and various emergency situations. Therefore, in the event of a special situation, e-commerce operators urgently need an optimized emergency response plan. This article aims to establish a logistics network scheduling model to help operators cope with emergencies and improve operational efficiency in emergency situations. The article establishes a path planning model that considers how the logistics network can quickly plan when a warehouse or road suddenly closes, which can meet the logistics transportation requirements and minimize the number of logistics adjustments required. The article focuses on the classification integration and visualization of logistics network data, evaluates the importance of each logistics location and road, determines the workload and efficiency, dynamically adjusts the logistics sites, and discusses the impact of logistics site closures and the feasibility of this research method. The planning model developed in this article has strong reference value for the mobilization plan of real logistics.

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ForeScanGuard: Proactive Monitoring and Detection for Sustainable Forest Conservation

ForeScanGuard: Proactive Monitoring and Detection for Sustai...

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International Conference on Inventive Computation Technologies (ICICT)

作者： R. Santhana Krishnan S. Balamurugan M. Rekha R Arul Jose K. Haribabu A. Essaki Muthu Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Mathematics Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering P.S.R.R College of Engineering Sivakasi India Department of Electrical and Electronics Engineering RajaRajeswari College of Engineering Bangalore India Department of Mechanical Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In response to the increasing environmental threats, ForeScanGuard emerges as a vital forest monitoring system. Integrating advanced sensors and a proactive anomaly detection algorithm, it employs a 5-channel fire sensor for early fire detection, ultrasonic and vibration sensors for identifying tree cutting activities, and a BMP120 sensor to confirm tree falls. GPS modules attached to trees allow real-time location tracking, transmitted via Wi-Fi to a cloud server. The ForeScanGuard Algorithm analyzes sensor data for anomalies like fire outbreaks, illegal logging, and tree falls. Its proactive alerts facilitate rapid responses from forest officials, ensuring effective forest management. ForeScanGuard, with multiple sensors and cloud-based analytics, represents a significant advancement in smart forest monitoring, promoting conservation efforts through real-time anomaly detection and informed decision-making. This comprehensive solution enhances ecosystem protection, fostering a sustainable and proactive approach to forest conservation.

关键词： Vibrations Cloud computing Forestry Real-time systems Environmental management Random forests Monitoring

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Music Emotion Recognition using Convolutional Neural Networks for Regional Languages

Music Emotion Recognition using Convolutional Neural Network...

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Ambient Intelligence, Knowledge Informatics and Industrial electronics (AIKIIE), International Conference on

作者： Shashidhar R Dhanush Balivada Shalini D N Krutthika Hirebasur Krishnappa Roopa M Department of Electronics and Communication Engineering JSS Science and Technology University Mysuru India Department of Computer Science Sothern University and A&M College Baton Rouge Lousiana Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru India

Music emotion recognition is a challenging task that has been getting increasing kindness in the field of artificial intelligence. We propose a unique lD Convolutional Neural Network (lD CNN) method for music emotion recognition. Our proposed approach combines the advantages of convolutional neural networks with the lD temporal structure of music signals. Evaluate our model on two publicly available datasets and demonstrate its effectiveness in accurately recognizing different emotions from music signals. Our method presents a novel approach to abstracting raw audio signals into meaningful representations. It utilizes multiple lD convolutional and pooling layers to extract features from the raw audio signals, which can then be used for further analysis. We demonstrate the effectiveness of our approach by providing examples of applications that can benefit from this abstraction process, such as sound recognition and speech recognition. Additionally, we discuss the potential of this method to be applied in other areas such as natural language processing and music generation. We compare the performance of our model with traditional machine learning (ML) methods and demonstrate that our lD CNN outperforms these traditional ML methods in terms of accuracy. We also discuss potential use cases for this technology and its implications for future research in audio signal processing. We show that our model achieves superior results in terms of accuracy and computational efficiency, making it a promising solution for music emotion recognition tasks. Our results suggest that the lD CNN approach is an effective tool for recognizing musical emotions with high accuracy

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5G Managed Services Induced Changes in Telecom Value Chain

5G Managed Services Induced Changes in Telecom Value Chain

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communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Sasmita Mohanty Laxmipriya Samal Debashisa Samal Sudhir K. Routray Department of Economics Management and Industrial Engineering University of Aveiro Aveiro Portugal Department of Electronics and Communication Engineering SoA University Bhubaneswar India Department of Computer Science and Engineering CMR University Bangalore India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

This paper analyzes the transformative influence of managed services on the telecommunications value chain amidst the arrival of 5G technology. Managed services have assumed multifaceted roles within contemporary telecom markets, reshaping the value chain significantly over the past decades. However, their significance in the 5G era surpasses that of preceding mobile generations, endowing network vendors with increased authority. The evolution is primarily attributed to the introduction of novel technologies and provisions inherent in 5G networks. Unlike previous generations, network operators find themselves unable to independently construct their 5G infrastructure, thereby shifting the strategic landscape in favour of vendors. This paradigm shift underscores the pivotal role of specialized vendors in optimizing 5G networks to address the unique challenges posed by this high-speed, low-latency technology. Managed services integration in 5G heralds a paradigmatic shift in telecom value chains, revolutionizing network design, deployment, and maintenance methodologies. In addition to traditional responsibilities, managed service providers now contribute to network security, proactive monitoring, and dynamic optimization, ensuring heightened reliability and efficiency. This transformative impact empowers vendors to navigate the complex telecom landscape and unlock the full potential of 5G technologies.

关键词： Technological innovation 5G mobile communication Shape Signal processing Telecommunications Telecommunication computing Outsourcing Telecommunication network reliability Research and development Optimization

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Efficient Real-Time Human Activity Recognition with IoT Sensor Data Using Conditionally Parametrized Convolutions

Efficient Real-Time Human Activity Recognition with IoT Sens...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Mayank Lovanshi Vivek Tiwari Anurag Singh Rajesh Ingle Department of Computer Science Engineering International Institute of Information Technology Naya Raipur India Department of Computer Science Engineering ABV-Indian Institute of Information Technology Gwalior India Department of Electronics Communication Engineering International Institute of Information Technology Naya Raipur India Department of Computer Science Engineering Symbiosis Skills of Professional University Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Human Activity Recognition (HAR) is a trading area in computer vision and deep learning. However, boosting the performance of deep learning models often necessitates increasing their size or capacity, which raises computational demands. The high operational costs present challenges for real-time HAR on sensor-based devices. Shallow learning approaches, while computationally efficient, generally fail to deliver comparable performance. Consequently, there is a critical need for deep learning methods that can effectively balance accuracy and computational cost, a topic that remains largely underexplored. This paper introduces a computationally efficient CNN that utilizes conditionally parametrized convolution, designed specifically for real HAR on sensor-based devices. The model is tested on the OPPORTUNITY dataset, achieving existing accuracy without increasing computational load. Through extensive ablation studies, we show that this high-capacity network surpasses baseline models while maintaining a similar level of computational efficiency. The proposed method can directly replace existing deep HAR frameworks and be easily implemented on sensor-based platforms for real-time applications.

关键词： Performance evaluation Deep learning Accuracy Costs Convolution Computational modeling Real-time systems Computational efficiency Human activity recognition Load modeling

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ANALYSIS OF CONCRETE CRACKS AND FATIGUE IN SMART CITIES USING YOLOV3

ANALYSIS OF CONCRETE CRACKS AND FATIGUE IN SMART CITIES USIN...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Archana, U. Sharma, Sarvdaman Singh, Sitesh Kumar Sureshkumar, R. Senthilkumar, P. Harish Babu, T. Department of Computer Science and Design R.M.K. Engineering College Chennai India Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Oromia Nekemte Ethiopia Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai India KL School of Architecture Koneru Lakshmaiah Education Foundation Greenfield Vaddeswaram Andhra Pradesh Guntur India Department of Electrical and Electronics Engineering Sethu Institute of Technology Tamilnadu India

ISBN: (纸本)9798350305708

This study focuses on the application of the YOLOv3 (You Only Look Once version 3) object detection algorithm for the analysis of concrete cracks and fatigue within Smart Cities. Concrete infrastructure plays a crucial role in urban environments, and its structural integrity is paramount for ensuring public safety and sustainable development. The proposed approach utilizes YOLOv3 to identify and localize instances of concrete cracks and signs of fatigue in images and videos captured from various urban settings. By leveraging deep learning techniques, this research aims to enhance the efficiency and accuracy of monitoring and maintaining concrete structures, contributing to the overall resilience and longevity of Smart Cities. The findings of this study present valuable insights into the potential of utilizing advanced computer vision methods for proactive infrastructure management in the urban landscape. © 2023 IEEE.

关键词： Concrete cracks Fatigue analysis Infrastructure monitoring Smart Cities YOLOv3

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Chromosome Image Enhancement for Efficient Karyotyping

Chromosome Image Enhancement for Efficient Karyotyping

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2022 International Conference on Innovative Trends in Information Technology, ICITIIT 2022

作者： Remya, R.S. Prasad, Hari Hariharan, S. Gopakumar, C. Department of Computer Science and Engineering Kerala India TKM College of Engineering Department of Centre for Artificial Intelligence Kerala Kollam India Regional Cancer Centre Department of Cancer Research Kerala Thiruvananthapuram India Department of Electronics and Communication Engineering Kerala India

ISBN: (纸本)9781665406703

Chromosome images are susceptible to sensor and staining noises, inhomogeneity, and blurring which prevent efficient karyotyping. In this research work, image processing methods are systematically extended for the preprocessing of chromosome images, and a novel approach for denoising and enhancing the chromosome images is proposed. The proposed approach is mathematically modeled and evaluated with subjective and objective measures. Promising results are obtained which are further substantiated with the post-classification of the segmented chromosomes from the preprocessed input image. Performance of the proposed method is quantified in terms of MSE (Mean Squared Error), PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity Index Measure), FSIM(Features Similarity Index Measure), SAM(Spectral Angle Mapper), and SRE(Signal to Reconstruction Error ratio). An MSE of 8.164, PSNR of 39.037, SSIM of 0.9654, SAM of 81.729, SRE of 63.842, and FSIM of 0.6128 are obtained, on average for a set of 10 test images which were previously degraded with Gaussian noise and Gaussian blur. Post-classification accuracy improved from 88% to 95% as and when the proposed preprocessing is followed by the classification task. © 2022 IEEE.

关键词： Image enhancement

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