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METHOD, APPARATUS AND DEVICE FOR CONSTRUCTING SPEECH RECOGNITION MODEL, AND STORAGE MEDIUM

机译:构建语音识别模型的方法,装置和装置和存储介质

摘要

A method, apparatus and device for constructing a speech recognition model and a storage medium, relating to the field of artificial intelligence. The method comprises: obtaining a plurality of training speech samples (101); constructing a speech recognition model by means of an independent convolution layer, a convolution residual layer, a fully connected layer, and an output layer (102); inputting training speech information to the speech recognition model, updating a weight of neurons in the speech recognition model with the speech information and a text tag corresponding to the speech information by means of a natural language processing (NLP) technology, and then obtaining a target model (103); evaluating an error of the target model by means of L(S) = -ln[Pi](h(x), z) being an element of a set Sp(z|h(x))= -sigma(h(x), z) being an element of a set Sln p(z|h(x)) (104); adjusting the weight of the neurons in the target model until the error is less than a threshold, and setting the weight of the neurons with the error less than the threshold as an ideal weight (105); and deploying the target model and the ideal weight on a client (106). The present invention reduces influence of tone in the speech information on a predicted text and computation burden in recognition process in the speech recognition model.
机译:一种用于构建语音识别模型和存储介质的方法,装置和装置,与人工智能领域有关。该方法包括:获得多个训练语音样本(101);通过独立的卷积层,卷积残余层,完全连接层和输出层(102)构造语音识别模型;向语音识别模型输入训练语音信息,通过语音信息和通过自然语言处理(NLP)技术对应于语音信息的语音识别模型中的神经元重量,然后获取目标型号(103);通过L(s)= -ln [pi] (h(x),z) 是一个元素来评估目标模型的错误设置S p(z | h(x))= -sigma (h(x) ,z) 是集S的元素 ln p(z | h(x))(104);调节目标模型中神经元的重量,直到误差小于阈值,并使误差小于阈值的神经元的重量,作为理想重量(105);并在客户端(106)上部署目标模型和理想重量。本发明减少了语音信息中的语音信息中的语音和计算负担对语音识别模型中的识别过程中的影响。

著录项

  • 公开/公告号WO2021051628A1

    专利类型

  • 公开/公告日2021-03-25

    原文格式PDF

  • 申请/专利权人 PING AN TECHNOLOGY (SHENZHEN) CO. LTD.;

    申请/专利号WO2019CN119128

  • 发明设计人 WANG JIANZONG;JIA XUELI;

    申请日2019-11-18

  • 分类号G10L15/06;G10L15/16;

  • 国家 CN

  • 入库时间 2022-08-24 17:57:12

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