基于机器学习的电力系统语音指令识别算法研究

Research on Speech Command Recognition Algorithm for Power Systems Based on Machine Learning

通过提高电力系统中语音指令识别技术的准确度、实时性和鲁棒性,旨在增强电力系统的可靠性和稳定性。首先分析了电力系统语音信号的预处理方法,包括信号增强、语音帧分割和频谱平滑等技术,在此基础上设计了一种基于高斯混合模型的语音指令识别算法。试验结果表明,该算法在电力系统语音控制场景下具有较高的识别准确率和实时性,同时具备良好的鲁棒性,完成能够满足电力系统复杂环境下的语音指令识别需求。研究还指出了一些改进和完善的方向,以进一步提升算法性能,满足更广泛的实际应用需求。

Enhancing the accuracy,real-time capability,and robustness of speech command recognition technology in power systems is crucial for improving system reliability and stability.This research first analyzes preprocessing methods for speech signals in power systems,including techniques such as signal enhancement,speech frame segmentation,and spectrum smoothing.An algorithm based on Gaussian Mixture Models(GMM)for speech command recognition is designed.Experimental results demonstrate that the algorithm achieves higher recognition accuracy and real-time performance in power system speech control scenarios,while maintaining robustness suitable for complex power system environments.The study also identifies areas for further improvement and enhancement to enhance algorithm performance for broader practical applications.