Power Autonomous Mobile Robot Operation and Inspection AI Assistant Speech Multiplex Signal Endpoint Segmentation
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 end-point segmentation operation.Robot operation inspection AI assistant voice service includes voice multi-channel signal acqui-sition,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 milt to calculate the transformation matrix,complete the diagonalization of the covariance matrix of the speech multi-channel signal samples,and input the feature infor-mation 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 segmenta-tion of speech multi-channel signal endpoints.
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