Vocal cord anomaly detection method based on acoustic phonetic features

Abstract

The invention discloses a vocal cord anomaly detection method based on acoustic phonetic features. The method comprises the steps of firstly, extracting mel frequency cepstral coefficient (MFCC), the fundamental frequency F0, the fundamental frequency perturbation Jitter, the amplitude perturbation Shimmer and the harmonics to noise ratio HNR for each frame of voices; adopting acoustic features as an input, and respectively training a Gaussian mixture model theta A and a Gaussian mixture model theta N based on the expectation-maximization EM algorithm, wherein the Gaussian mixture model theta A and the Gaussian mixture model theta N respectively represents the vocal cord abnormal state and the vocal cord normal state; finally, respectively inputting the feature matrix F of the test voice into the Gaussian mixture model theta A and the Gaussian mixture model theta N so as to obtain a corresponding output probability P (F|theta A) and a corresponding output probability P (F|theta N). If the P (F|theta A) is larger than the P (F|theta N), the vocal cord of the speaker of the test voice is abnormal. Otherwise, the vocal cord of the speaker of the test voice is normal. According to the technical scheme of the invention, multiple sets of acoustic features, adopted as the inputs of Gaussian mixture models, are extracted from the test voice, wherein the acoustic features can effectively reflect the state of the vocal cord. Therefore, voices in the vocal cord abnormal state and in the vocal cord normal state can be effectively distinguished. As a result, whether the vocal cord of the speaker of the test voice is abnormal or not can be judged. The method has the advantages of non-intrusion, convenience, low cost and the like.

Description

technical field [0001] The invention relates to speech signal processing and machine learning technology, in particular to a vocal cord abnormality detection method based on speech acoustic features. Background technique [0002] Speech is one of the important means of human communication, and speech communication barriers seriously affect people's normal life. Vocal cord lesions are one of the main factors leading to speech communication impairment. Accurate diagnosis of vocal cord abnormalities is the premise of pronunciation rehabilitation, which is especially important in clinical medicine. The traditional detection method of vocal cord abnormality is to use laryngoscope or electroglottograph. The detection method based on the laryngoscope is invasive. Inserting the laryngoscope into the tester's throat requires the tester's utmost cooperation, which will bring unacceptable pain to the tester. In addition, during the laryngoscopy test, the tester cannot make a normal ...

AI Technical Summary

Problems solved by technology

The detection method based on the laryngoscope is invasive. Inserting the laryngoscope into the tester's throat requires the tester's utmost cooperation, which will bring unacceptable pain to the tester

In addition, during the laryngoscopy test, the tester cannot make a normal voice, and cannot detect the vibration of the vocal cords, which has certain limitations

The detection method based on the electroglottograph requires two electrode plates to be closely attached to the tester's neck, which will also bring discomfort to the tester. It is not suitable for people with obesity in the neck, especially the elderly, women and children

Images