# Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection
## 4 Results and Discussion

Table 5: The accuracy (%) of different thresholds \(\epsilon\)(rr) under the four types of machine learning. abnormal. In addition, some isolated points on the RP of the pathological glottal signal indicated that the pathological voice signal was not stable, and there were anti-correlation processes and weighty fluctuations. Compared with the method proposed in this article, the features from the other works in recent years were applied for pathological voice detection. They all used 10-fold cross-validation. Moreover, the experiment in the MEEI database adopted the same classifier: BN. The results are listed in Table 6. All the evaluation indicators in the table are closer to 1, which indicates the better effect and robustness of the feature. Refs. [27,28] both showed an accuracy of nearly 90%, indicating that RQA has a strong potential in pathological voice detection. Nevertheless, they did not make any improvements to RQA. They just used and explored combined RQMs. The method proposed in this article analyzed the signal itself and selected the best recurrence threshold to draw a more accurate RP structure, using  
Figure 4: Three-dimensional scatter plots of MRQMs. Figure 5: RPs of normal glottal signal and pathological glottal signal. 13 RQMs to quantify recurrence features. The accuracy also showed that the method in this article was better than the method of directly extracting RQMs for pathological voice detection. The accuracy was improved by 8 % and 12.45%, respectively, for RQMs in [27] and RQMs in [28]. MFCC are typical characteristic parameters of acoustic tasks. They achieved 92% recognition results, respectively, from raw voice signals. Traditional cepstral characteristic parameters can characterize the differential changes of the vocal tract system. They cannot describe the changes in the vocal fold's vibration mechanism directly caused by voice diseases, so they cannot characterize all pathological voice information. The accuracy of the nonlinear feature combination was only 75.56%, which is far inferior to the traditional MFCC. The short-term non-stationary characteristics of speech signals limited the application of traditional nonlinear time series analysis methods. The method proposed in this paper can quantitatively analyze the dynamic nonlinear behavior of different aspects of the speech dynamic system by using RQMs. MRQMs combined with machine learning achieved the best results in terms of accuracy rate, kappa value, precision, recall rate, F-Measure, ROC area and so on. MRQMs analyzed the glottal signal from the perspective of vocal fold vibration, filtering out lip radiation's influence on pathological voice detection. They quantified the recurrence characteristics of different frequency bands under the ERB scale. MRQMs embodied excellent detection performance and application prospects. In this paper, multi-classification experiments used normal voice samples, vocal folds paralysis samples and vocal folds polyp samples. From the confusion matrix in Figure 6, the information of the multi-classification experiment can be further viewed. The true positive accuracy of normal voice and vocal folds paralysis was more than 90% in the multi-classification experiment. Vocal fold polyps were confused with other normal voices and vocal folds paralysis categories in the multi-classification experiment. Other indicators of multi-classification experiment results are also listed in the Table 7.  
Compared with the original voice signal, the glottal signal removes the effects of lip radiation and so on, so that it can better reflect the characteristics of vocal folds vibration. MRQMS used the glottal signal as the raw signal and used the human auditory perception characteristics to extract multi-scale recurrence features. From the point of view of nonlinear dynamic recurrence, although the pathological voice was a relatively deterministic signal, there was a break in the diagonal of the pathological voice signal.