# Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection
## 3 Material and Evaluation  
Pathological voice contains a wealth of information, including not only the original voice information to be conveyed but also the speaker's age, gender and voiceprint. Inorder to evaluate the method proposed in this work, we utilized the Massachusetts Eye and Ear Infirmary (MEEI) database [43], which contains the normal and pathological voice /a:/ with the patient's expert diagnosis results, gender and smoking status. The vowel /a:/ sound will be more affected by acoustic characteristics. This database selected 53 normal voice samples and 173 pathological voice samples with various disorders as a data subset. This subset was selected by considering different voice disorders, gender and age mean and standard deviation of normal and pathological voice databases [44]. The statistics of the selected voice samples in the MEEI database in this work is shown in Table 3. All voice disorders are listed up in a Table 4. Among them, 53 normal voice samples, 20 cases of vocal fold polyp samples and 67 cases of vocal fold paralysis samples were selected as multi-class samples. All voice signals were sampled with 16-bit resolution. The sampling rate of healthy voice signals was 50 kHz, and that of pathological voice signals was 25 kHz or 50 kHz. Before all the processing, we had down-sampled all the voice signals to 25 kHz. This work presented multi-scale recurrence quantification measurements for voice disorder detection and classification. We use Matlab 2020b software to extract this feature. Thereinto, the cross recurrence plot toolbox, was used to build recurrence plots and extract recurrence quantification measurements [45]. All machine learning algorithms and testing are done using Waikato Environment for Knowledge Analysis software. Four traditional machine learning algorithms were utilized for pathological voice detection experiments. After many times of parameter adjustment and optimization, the final model parameters were determined as follows: (1) The support vector machine (SVM) utilized a PolyKernel kernel function. The penalty factor was one. (2) The random forest (RF) utilized two hundred trees, and the maximum depth was four. (3) The Bayesian network (BN) utilized the tree augmented naive Bayes algorithm. (4) The Local Weighted Learning (LWL) utilized random forest classifier and FilteredNeighbourSearch. In addition, the four machine learning algorithms were utilized for pathological voice classification experiments. (1) The support vector machine utilized a PUK kernel function, and the penalty factor was two. (2) The random forest utilized fifty trees, and the maximum depth was twenty. (3) The Bayesian network utilized the K2 search algorithm. (4) The Local Weighted Learning utilized random forest classifier and FilteredNeighbourSearch. Due to the problem of unbalanced pathological samples, the FC-SMOTE algorithm was adopted for sample balancing for multi-classification experiments [46]. A 10-fold cross-validation was used to test the accuracy of the algorithm. All voice samples were divided into ten equal parts. In each validation, nine folds were selected as training sets and one-fold as test sets. The process was repeated ten times, so all ten pieces of data were all tested as test sets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Disorder** & \begin{tabular}{c} Vocal cord lesions \\ 1 \\ \end{tabular} & \begin{tabular}{c} Sulcus vocalis \\ 3 \\ \end{tabular} & \begin{tabular}{c} Vocal fold paralysis \\ 67 \\ \end{tabular} & \begin{tabular}{c} Vocal Fatigue \\ 2 \\ \end{tabular} & \
\begin{tabular}{c} Vocal Cords removed \\ 3 \\ \end{tabular} &
\begin{tabular}{c} Vocal cord oedema \\ 43 \\ \end{tabular} \\ \hline
**Disorder** & \begin{tabular}{c} Vocal palsy \\ 1 \\ \end{tabular} & \begin{tabular}{c} vocal polyp \\ 20 \\ \end{tabular} & \begin{tabular}{c} Vocal nodule \\ 18 \\ \end{tabular} & \
\begin{tabular}{c} Increased thickness of vocal cords \\ 1 \\ \end{tabular} &
\begin{tabular}{c} Vocal tremor \\ 13 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}