| 电力自主移动机器人运检AI助手语音多路信号端点切分 |
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| 引用本文：杨洋，宋祉霖，豆朝宗.电力自主移动机器人运检AI助手语音多路信号端点切分[J].计算技术与自动化,2024,(4):53-58 |
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| 中文摘要:研究了电力自主移动机器人运检AI助手语音多路信号端点切分仿真，解决语音端点切分运算复杂、不准确问题。机器人运检AI助手语音服务包括语音多路信号采集、播报、理解、控制等，采集到的AI助手语音多路信号经客户端降噪处理后，通过主分量分析筛选语音多路信号矩阵的特征值和特征向量，确定最有用因素作为基底向量，获取语音多路信号中最典型特征，经线性区分分析使语音多路信号的特征布局集中，获取用于切分的特征并构建变化矩阵，利用MLLT计算变化矩阵，完成语音多路信号样本的协方差矩阵对角化，并将特征信息作为服务端卷积神经网络预测模型输入，从帧级上实时分类语音多路信号数据，实现语音多路信号端点的切分。仿真实验结果显示：该方法可有效去除语音多路信号噪声，并完成语音多路信号端点准确切分。 |
| 中文关键词:语音多路信号 机器人 AI助手 语音特征提取 预测模型 端点切分 |
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| Power Autonomous Mobile Robot Operation and Inspection AI Assistant Speech Multiplex Signal Endpoint Segmentation |
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| Abstract:This paper studies the simulation of voice multi-channel signal endpoint segmentation of the AI assistant of electric power autonomous mobile robot operation inspection, and solves the problem of complex and inaccurate voice endpoint segmentation operation. Robot operation inspection AI assistant voice service includes voice multi-channel signal acquisition, broadcasting, understanding, control, etc. after the collected AI assistant voice multi-channel signal is denoised by the client, the eigenvalues and eigenvectors of the voice multi-channel signal matrix are screened through principal component analysis, and the most useful factors are determined as the base vector to obtain the most typical features in the voice multi-channel signal. After linear discrimination analysis, the feature layout of the voice multi-channel signal is centralized, Obtain the features used for segmentation and construct the transformation matrix. Use mllt to calculate the transformation matrix, complete the diagonalization of the covariance matrix of the speech multi-channel signal samples, and input the feature information as the convolution neural network prediction model of the server, classify the speech multi-channel signal data in real time from the frame level, and realize the segmentation of the speech multi-channel signal endpoint. The simulation results show that this method can effectively remove the noise of speech multi-channel signals and complete the accurate segmentation of speech multi-channel signal endpoints. |
| keywords:voice multi-channel signal robot AI assistant speech feature extraction prediction model endpoint segmentation |
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