Introducing an AI-driven lipreading system adept at decoding speech across diverse languages, including English, Tamil, and Telugu. Leveraging a deep learning architecture comprising a 3D Convolutional Neural Network ...
详细信息
ISBN:
(数字)9798350349900
ISBN:
(纸本)9798350349917
Introducing an AI-driven lipreading system adept at decoding speech across diverse languages, including English, Tamil, and Telugu. Leveraging a deep learning architecture comprising a 3D Convolutional Neural Network (3DCNN) with Bidirectional Long Short-Term Memory (BiLSTM) units, the model achieves remarkable accuracy in transcribing spoken words solely from visual cues provided by lip movements. Addressing critical accessibility needs, this system holds promise for applications in assistive technologies and human-computerinteraction systems. through rigorous experimentation and evaluation, the lipreading model demonstrates an impressive overall accuracy of 98.4%, underscoring its efficacy and robustness in recognizing spoken words across multiple languages. Advanced evaluation techniques, including ROC curves, confusion matrices, and classification reports, provide comprehensive insights into the model’s performance, enabling targeted refinements and optimizations. this work represents a significant advancement in the field of lipreading, offering a valuable contribution to multimodal communication and fostering inclusivity in diverse linguistic contexts.
the traditional seismic monitoring network is a product of the era of small data. After an earthquake occurs, there are usually more than ten observation station data participating in event analysis. these data are st...
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ISBN:
(数字)9798350373233
ISBN:
(纸本)9798350373240
the traditional seismic monitoring network is a product of the era of small data. After an earthquake occurs, there are usually more than ten observation station data participating in event analysis. these data are stored using traditional MySQL databases. the calculation process often adopts a human-computerinteraction method. But withthe implementation of early warning projects, high-density seismic observation networks have been built. this new type of seismic network is a strong support for second magnitude earthquake warning. But at the same time, it also generated a massive amount of observational data, the earthquake monitoring network has entered the era of big data. Traditional data storage and calculation methods do not meet the needs of earthquake warning systems, because earthquake warning systems need to analyze a large amount of data and quickly produce results. During the implementation of the warning project, Tianjin Earthquake Agency designed a earthquake warning network system based on cloud computing. the project team designed the overall system architecture using SaaS technology, and used a simple intensity meter to collect strong motion data. the system adopts concurrent filtering and reception technology to timely collect and process massive data produced by seismic stations. the project team has designed a software architecture for the seismic wave simulation display model based on web pages. Business personnel use High Chart JS software in the browser to simulate and display seismic waveforms. the system can carry out automatic detection and management of earthquake event triggering. through the evaluation of the system and the analysis of historical earthquakes, it has been proven that the system can accurately provide early warning of earthquake occurrence, which is superior to the performance of existing earthquake warning systems.
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