A speech signal feature learning method based on the first derivative of Mel spectrum
A speech signal, first derivative technology, applied in speech analysis, instruments, etc., to achieve the effect of high speed and scalability, good discrimination, and less training time
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[0026] The application will be described in further detail below in conjunction with the accompanying drawings. It is necessary to point out that the following specific embodiments are only used to further illustrate the application, and cannot be interpreted as limiting the protection scope of the application. The above application content makes some non-essential improvements and adjustments to this application.
[0027] combine figure 1 , figure 2 As shown, the speech signal feature learning method based on the mel spectrum first derivative of the present invention comprises the steps:
[0028] Step 1. Input disease speech samples and healthy speech samples;
[0029] Step 2. Framing all samples, detecting speech endpoints, extracting the first derivative of Mel spectrum MFCC with respect to time DMS (first Derivative of Mel-Spectrogram), and using matrix A for each sample i express;
[0030] The analysis of MFCC is based on the auditory principle of the human ear, whic...
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