## 1 Introduction  
A disturbance in the voice has profound implications for a person's social and professional life. In patients with progressive pathologies, it is essential to have access to a rapid diagnosis to promote better treatment and prognosis [1]. Some tests can be performed to detect voice pathologies, such as laryngoscopy, endoscopy, or stroboscopic exams. However, they are more or less invasive tests, at least from the patient's point of view, and they cause discomfort [2]. Electroglottography (EGG) is a non-invasive technique that allows measuring the vibration of the vocal folds during speech and singing [3]. It can provide important information about the function of the vocal folds and phonation, as well as the durations of glottic closure and opening patterns [4]. It can be combined with other diagnostic techniques, such as videostroboscopy, for a more comprehensive evaluation of the vocal folds [5]. This technique is helpful in voice therapy, as it allows real-time feedback and assessment of the patient's progress with the treatment being performed [6]. However, this technique has some limitations, such as sensitivity to skin conductance changes and difficulty obtaining clear signals in some individuals [7]. This technique is not used in this work since it is necessary to use an electroglottograph, which is not always available. Acoustic analysis is a non-invasive technique that allows the determination of the individual's voice quality and allows a pre-diagnosis, avoiding several invasive tests. It is a technique that can be used to diagnose and study vocal pathologies as it will enable measuring the acoustic signal properties of recorded speech or sustained vowels [8; 9; 10]. Several acoustic parameters can be used in the evaluation and classification of vocal pathologies, such as Peak Slope (PS), a ratio of the first harmonic to the second harmonic energy (H1/H2), Normalized Amplitude Quotient (NAQ), Cepstral Peak Prominence (CPP), Mel-Frequency Cepstral Coefficients (MFCCs), Autocorrelation, Harmonic to Noise Ratio (HNR), and Noise to Harmonic Ratio (NHR) [11; 12; 13]. The parameters Autocorrelation, Harmonic to Noise Ratio, and Noise to Harmonic Ratio are used by several authors as features to describe voice quality in assessment tools for voice pathologies [14; 15; 16; 17; 18; 19]. Concerning the pathological voices classification tools, several models have been used. [PERSON] et al., 2016 [14], used the k-Nearest Neighbors (k-NN) algorithm to identify and classify pathological voices from normophonic voices. [PERSON] and inam, 2021 [17], used a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) recurrent neural network model in their internal structure to classify pathological and normophonic voices. [PERSON] et al., 2019 [20], used LSTM and CNN to classify four voice disturbance classes, dysphonia, chronic laryngitis, vocal cord paralysis, and normophonic voices. In previous work, [PERSON] et al., in 2020 [21] used several parameters, including Autocorrelation, HNR, and NHR, and performed a statistical analysis using the boxplot tool to compare several pathologies. The authors have identified three clusters of pathologies. [PERSON] et al., 2018 [22], intended to classify chronic laryngitis and normophonic subjects. They used LSTM and Feedforward Artificial Neural Network (ANN) classifiers and Autocorrelation, relative jitter, and relative shimmer input parameters. In this binary classification, an accuracy of 99% was achieved. In [PERSON] et al., 2018 [23], several combinations of input features were experimented with, including HNR, using a Support Vector Machine (SVM) classifier to classify between pathological (a set of three pathologies) and normophonic voices. The author's best accuracy was 71%. Several authors have developed pathological voice identification systems, that allow the identification of some pathologies using different parameters.