Vocal cord-larynx ventricle-vocal track linked physical model and mental pressure detection method

A technology of vocal tract linkage and physical model, applied in psychological devices, diagnostic recording/measurement, medical science, etc., can solve the problem of low discrimination, lack of stability and robustness, and difficulty in improving the reliability of variant speech classification, etc. question

Active Publication Date: 2016-06-15
HOHAI UNIV CHANGZHOU
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the variant speech under mental stress, especially the variant speech under multi-tasking brain load pressure, has a relatively low degree of auditory discrimination, and the general acoustic features cannot classify it correctly, lacking stability and robustness
In addition, since the generation mechanism of variant

Method used

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  • Vocal cord-larynx ventricle-vocal track linked physical model and mental pressure detection method
  • Vocal cord-larynx ventricle-vocal track linked physical model and mental pressure detection method
  • Vocal cord-larynx ventricle-vocal track linked physical model and mental pressure detection method

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Embodiment 1

[0075] Such as figure 1 As shown, a physical model of vocal cord-larynx chamber-vocal tract linkage of the present invention includes:

[0076] A set of mechanical equations describing the movement pattern of the vocal cords, and a set of aerodynamic equations describing the corresponding pressure drop distribution along the depth of the glottis and in the direction of the laryngeal chamber, false vocal cords, and vocal tract.

[0077] Specifically, the set of mechanical equations includes:

[0078] m 1 d 2 x 1 dt 2 + r 1 dx 1 d t + s 1 ( ...

Embodiment 2

[0112] On the basis of Embodiment 1, Embodiment 2 provides a stress detection method based on speech generation modeling, including:

[0113] Step S1, establishing a physical model of vocal cord-laryngeal chamber-vocal tract linkage;

[0114] Step S2, using the physical model to generate an analog voice signal under a corresponding pressure in the real world;

[0115] Step S3, according to the physiological parameter estimation algorithm, estimate the corresponding physiological parameters of the speaker when speaking under the corresponding pressure state, so as to establish the corresponding physiological characteristic relationship of the speech signal;

[0116] In step S4, the mental stress is detected according to the physiological characteristic relationship.

[0117] This pressure detection method adopts the fitting method of Analysis by Synthesis to establish the connection between the model and the real voice data, compare the waveform generated by the model with the...

Embodiment 3

[0133] On the basis of embodiments 1 and 2, this embodiment 3 also provides a voice-based physiological parameter estimation algorithm, including the following steps:

[0134] Step S1 ', obtain the frequency spectrum of real speech and the initial value of quadratic fitting;

[0135] Step S2', perform quadratic fitting, to construct the cost function based on the global characteristic of speech frequency spectrum;

[0136] Step S3', estimate the physiological parameters according to the cost function.

[0137] The method for obtaining the frequency spectrum of real speech and the initial value of quadratic fitting in described step S1 ' comprises the steps:

[0138] Step S11', obtain the sound source information of real speech by linear prediction, i.e. the residual signal;

[0139] Step S12', perform Fourier transform on the residual signal to obtain the spectrum of real speech, and separate the high-frequency component and low-frequency component from the residual signal t...

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Abstract

The invention relates to a vocal cord-larynx ventricle-vocal track linked physical model and a mental pressure detection method. The physical model includes a mechanical equation set for describing a vocal cord motion model, and an aerodynamics equation set for describing pressure drop distribution in a glottis depth direction and a larynx ventricle-false vocal cord-vocal track direction. A physiological parameter estimation algorithm is designed through the established vocal cord-larynx ventricle-vocal track linked physical model, so that a physiological variation mechanism of phonation in a pressure state is researched. Physiological feature parameters of the vocal cords and the larynx ventricle when a speaker phonates in the pressure state are extracted, and a relation from real voice signals to physiological features is established. According to the estimated physiological parameters, variation features of various vocal organs and the flow state of airflow in the vocal organs under the influence of pressure variation factors are obtained, and the variation features are used for detection of the mental pressure. The detection recognition precision and reliability are improved.

Description

technical field [0001] The invention relates to the field of intelligent voice technology, in particular to a method for detecting mental stress by using voice technology. Background technique [0002] Psychological stress refers to the comprehensive physical and mental reflection when people find that real or imagined things exceed their expectations. Psychology shows that too much stress can lead to negative and painful stress responses, triggering negative emotions such as depression, tension, anxiety and anger, resulting in a decline in work efficiency and quality of life. Psychologists believe that long-term living under the pressure of life will lead to serious mental illness, and increase the sensitivity of the human body to disease, and even lead to cancer. There is a significant correlation between emotion and stress. Psychological stress can induce a variety of emotions, such as excitement, loss, boredom, etc., and under different levels of stress sources, it will...

Claims

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Application Information

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IPC IPC(8): G10L25/51G10L25/63A61B5/00A61B5/16
CPCA61B5/165A61B5/4884G10L25/51G10L25/63
Inventor 姚潇徐宁汤一彬张杰高明生蒋爱民刘小峰张学武
Owner HOHAI UNIV CHANGZHOU
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