# An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing
## 1 Introduction
### Issue
The Online Sequential Extreme Learning Machine (OSELM) is one of the more noteworthy machine learning methods that can be considered a more current technique, as well as a faster and more accurate algorithm than classic adaptations in the categorization process of voice disorders [28]. The majority of recent research falls into the latter category, as the focus has shifted to deep learning applications and notable variations of convolutional and recurrent neural networks [34; 35]. Deep learning can manage more extensive data sets and distinguish more diverse speech characteristics, potentially outperforming medical experts in various vocal pathology classification tasks. [PERSON] et al. showed this in their deep learning approach by employing 12 Mel-frequency cepstral coefficients from each speech sample as row attributes or as cepstrum vectors [36; 37; 38]. [PERSON] et al. showed that MFCC-based approaches could be enhanced by including gender-specific information [38]. By extracting and analyzing variable-length speech signal segments utilizing the prisms of the energy, primary tone, and spectrum, [PERSON] et al. proposed assessing several characteristics of the voice signal window as low-level descriptors [39]. They then trained their model using the obtained data. Various functional factors, including moments, extremes, percentiles, and regression parameters, may be provided for the network, resulting in a collection of aggregate characteristics for healthy and impaired speech. In order to show how multipath learning and learning transfer applications could be used in accordance with the multifunctional LSTM-RNN paradigm, [PERSON] et al. collected features from voice samples of the vowel sound /a:/ and computed the Mel-frequency cepstral coefficients (MFCCs), which can be used to differentiate between patients with laryngeal cancer and healthy controls [40; 41]. Similar findings in additional studies indicated that the recurrent neural network's accuracy was comparable to CNN, and the predicted outcomes were almost identical. [PERSON] et al. proposed filtering the input voice signal using deep learning, followed by a decision-level fusion of deep learning with a non-parametric learner [40; 41; 42; 43]. [PERSON] demonstrated that employing suitable speech enhancement pre-processing improves the accuracy of automated categorization of vocal diseases by reducing the inherent noise caused by voice impairment [44; 45]. The FC-SMOTE approach described by [PERSON] et al. handles the original class-imbalanced dataset and outperforms standard imbalanced data oversampling algorithms [45]. The application of kernel-based extreme learning machines, data pre-processing, a combination of the k-means clustering-based feature weighting approach and a complex-valued artificial neural network, and other techniques were also demonstrated [23; 45; 46]. [PERSON] et al. utilized a hybrid system using microphones and electroglottography (EGG) sensorsas inputs. These signals are converted into spectrograms and input into a pre-trained convolutional neural network (CNN). The CNN features are merged and processed using a bi-directional long short-term memory network [47]. Analagic hybrid fusion produced by [PERSON] et al. achieved a similar outcome [48]. Despite the favorable results which were produced utilizing classic approaches machine learning and modern deep learning techniques, some worries and unresolved difficulties, such as the correct prediction of a large number of characteristics, remain [49]. There is a scarcity of medical voice training data for machine learning models. Furthermore, comprehending voice-impacting illnesses and their varieties is more complicated than other activities, and the result often is valid only in narrow given conditions. Finally, pathological voice is inherently noisy, complicating the algorithms with real-life data even further [44].