Lie detection method based on depth recursion-type condition-restricted Boltzmann machine

A technology with restricted Boltzmann machines and conditions, applied in diagnostic recording/measurement, medical science, psychological devices, etc., which can solve the problems of bias and high cost of polygraph detection

Active Publication Date: 2018-06-19
SOUTHEAST UNIV
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Problems solved by technology

[0005] Purpose of the invention: In order to address the deficiencies of the prior art and solve the problems of excessive cost and deviation in the use of empirical info

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  • Lie detection method based on depth recursion-type condition-restricted Boltzmann machine
  • Lie detection method based on depth recursion-type condition-restricted Boltzmann machine
  • Lie detection method based on depth recursion-type condition-restricted Boltzmann machine

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[0046] The present invention will be further described below with reference to the drawings and specific embodiments.

[0047] figure 2 Is the structure diagram of the restricted Boltzmann machine, Is the unit value of visible layer node i at time t, Is the unit value of node k in the visible layer at time t-p. h j The variable of the jth node of the hidden layer h. σ i Is the variance of node i in the visible layer. b i And c j Is the offset between the visible layer node i and the hidden layer node j. It is the weight matrix of the directed connection between visible layer nodes from time t-p to time t. It is the weight matrix of the directed connection from the visible layer node to the hidden layer node at t-p. W vh It is the weight matrix of the symmetric connection between the visible layer node and the hidden layer node at time t. Given the past observations of multiple visible layers Hidden layer node The observed value of the visible layer node at the current ...

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Abstract

The invention discloses a lie detection method based on a depth recursion-type condition-restricted Boltzmann machine. Firstly, in a continuous voice paragraph, by using the fact that the condition-restricted Boltzmann machine has good modeling features and a simple reasoning process on a time series, a training sample is modelled to obtain high-order statistic information about whether or not a speaker lies; then, supervised parameter training is conducted on a recurrent neural network by using the high-order statistic information and the label of the training sample. After initialization parameters of the condition-restricted Boltzmann machine and the recurrent neural network are obtained, the two basic network units are constructed from bottom to top; on a validation data set, the parameters of the recurrent neural network are finely adjusted based on least square regression; by using the constructed networks, voice signal features of the speaker are tested. According to the method,the result of lie detection can be obtained automatically, and the method has a relatively-high recognition rate, low requirements on the professional knowledge and skills of evaluators and high testefficiency.

Description

technical field [0001] The invention relates to a speech polygraph technology, in particular to a method for polygraph detection by utilizing the speech information of the speaker's context. Background technique [0002] The basic principle of "polygraph detection" is that the psychological changes of people when lying will inevitably cause changes in some physiological parameters (such as skin electricity, heartbeat, blood pressure, breathing brain waves, voice), usually it is only restricted by autonomic nerves and difficult to be affected by brain consciousness. control. Therefore, the traditional polygraph technology is a combination of psychology and physiology and other disciplines, and detects the intention and state of the individual's inner concealment through the electrophysiological parameter testing system. At present, a large amount of psychological work is based on facial expressions, physiological activities and gestures as test clues for lies. There are thr...

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Application Information

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IPC IPC(8): A61B5/16
CPCA61B5/164A61B5/4803A61B5/7235
Inventor 赵力查诚魏昕徐新洲黄程韦塔什甫拉提·尼扎木丁余华邹采荣
Owner SOUTHEAST UNIV
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