Development of an Interactive Oral English Translation System Leveraging Deep Learning Techniques

An advanced interactive English oral automatic translation system has been developed using cutting-edge deep learning techniques to address key challenges such as low success rates, lengthy processing times, and limited accuracy in current systems. The core of this innovation lies in a sophisticated deep learning translation model that leverages neural network architectures, combining logarithmic and linear models to efficiently map and decompose the activation functions of target neurons. The system dynamically calculates neuron weight ratios and compares vector levels, enabling precise and responsive interactive translations. A robust system framework is established around a central text conversion module, integrating hardware components such as the I/O bus, I/O bridge, recorder, interactive information collector, and an initial language correction unit. Key hardware includes the WT588F02 recording and playback chip (with external flash) for audio recording and NAND flash memory for efficient data storage. Noise reduction is achieved using the POROSVOC-PNC201 audio processor, while the aml100 chip enhances audio detection capabilities. The extensive neuron network testing using a dataset of 1.8 million translation samples demonstrates the system's superior performance, achieving an impressive success rate exceeding 80%, a rapid translation time of under 50ms, and a remarkable translation accuracy of over 95%. This state-of-the-art system sets a new benchmark in interactive English oral translation, achieving a success rate exceeding 80% (a 10% improvement over existing methods), a rapid translation time of under 50ms (a 30% reduction), and a remarkable translation accuracy of over 95% (a 5% improvement), by combining deep learning advancements with high-performance computing and optimized hardware integration.