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Deep learning-based voice fatigue detection method

A technology of voice detection and deep learning, which is applied in medical science, psychological devices, diagnostic recording/measurement, etc., can solve the problems of not considering the impact of the voice response of the object of physical function detection, so as to improve the accuracy of judgment and reduce the psychological resistance Effect

Active Publication Date: 2022-04-29
NANTONG INST OF TECH
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Problems solved by technology

[0005] The purpose of the present invention is to provide a voice detection fatigue method based on deep learning, which is used to solve the technical problem that the comprehensive analysis of various acoustic features is lacking in the prior art, and does not consider the physical function in the process of exercise. The effect of the presence of changes on the speech response of the test subject

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  • Deep learning-based voice fatigue detection method
  • Deep learning-based voice fatigue detection method
  • Deep learning-based voice fatigue detection method

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Embodiment Construction

[0056] The specific implementation of the present invention will be described in further detail below by describing the embodiments with reference to the accompanying drawings, so as to help those skilled in the art have a more complete, accurate and extended understanding of the inventive concepts and technical solutions of the present invention.

[0057] Such as Figure 2-7 As shown, the present invention provides a kind of voice detection fatigue method based on deep learning, comprises the following steps:

[0058] 1. Collect the corpus of exercise subjects at different times during the exercise, and establish a corpus for storing the corpus.

[0059] Considering the impact of the quality of the original speech signal on the recognition performance of the system, at the same time, the academic community lacks a dedicated motion fatigue detection corpus. Therefore, the preliminary work of this method includes the establishment of the SUSP-SFD sports fatigue corpus in the e...

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Abstract

The invention discloses a deep learning-based voice fatigue detection method, which comprises the following steps of: 1, collecting corpora of an exercise subject, and establishing a corpus for storing the corpora; 2, performing fatigue grade division and processing and marking corpora in a corpus; 3, performing data preprocessing on the selected corpus; 4, extracting a plurality of acoustic characteristic parameters of the corpus in the corpus; 5, establishing a BLSTM neural network model, and training to obtain a fatigue analysis model; meanwhile, establishing a segmentation analysis model to determine a segmentation correction value; 6, performing corpus collection on a user to be detected, and performing preprocessing and feature extraction on the obtained corpus; and 7, inputting the plurality of acoustic characteristic parameters obtained in the previous step into an analysis model, and outputting the current fatigue grade of the user. According to the method, the influence of the change of the body function in the movement process on the voice response of the detected object is not considered, and the comprehensive analysis result of the acoustic characteristic parameters is more reliable and accurate.

Description

technical field [0001] The invention relates to the technical field of automobile intelligent control, in particular to a voice detection fatigue degree method based on deep learning. Background technique [0002] Sports fatigue detection technology plays an important role in sports training and helps to improve the scientific validity of sports fatigue detection. At present, fatigue detection methods in academia can be divided into two aspects: subjective detection and objective detection. Subjective fatigue testing methods are mainly based on subjective perception to assess fatigue, such as subjective questionnaires, the Stanford Sleep Scale, and the Sleep Habits Questionnaire. Objective detection methods mainly use instruments and equipment to detect the psychological, physiological and biochemical indicators of the human body. Specifically, first, the physiological signals of athletes are detected, including surface electromyographic signals, electroencephalogram signa...

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Application Information

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IPC IPC(8): A61B5/16A61B5/00
CPCA61B5/16A61B5/4803
Inventor 陈枢茜孙溢洋
Owner NANTONG INST OF TECH
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