# An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing
## 1 Introduction
### Issue
The use of machine learning algorithms to automate the diagnosis of diseases using variations in human speech has piqued the interest of medical researchers [14]. A State of the Art Review in Machine Learning Applications for Vocal Pathology Detection and Analysis  
The human voice production system is a complex natural mechanism capable of changing pitch and volume. The section in which folding is the principal source of underlying internal and external elements frequently destroys the rationale of vocal folds [15]. While many approaches for detecting voice pathology have been proposed in recent research, they tend to focus on distinguishing normal voices from pathological sounds [12; 14; 16]. The aforementioned papers focus on the design of the algorithm, while neglecting to test it with the clinical professionals whom the software is supposed to help or even replace. Additionally, the classification into healthy and pathological voices proves the concept, but does not track the scope of the problem or any changes over time. Foregoing the comparison of the final algorithm to its human counterparts and the binary classification approach eventually hinders the practical application of the research. In the recent study, in order to identify, extract, and evaluate substitution voicing following laryngeal oncosurgery, we proposed employing convolutional neural networks for the categorization of speech representations (spectrograms). The proposed algorithm takes an input of a Mel-frequency spectrogram as an input of deep neural network architecture. This approach has shown the best true-positive rate of any of the compared state-of-the-art approaches, achieving an overall accuracy of 89.47% [16]. In the present study, the acoustic substitution voicing index (ASVI) model was elaborated on, accomplishing quantification of SV assessment. The ASVI results were comparable to the auditory-perceptual SV evaluation performed by medical professionals. Therefore, the ASVI demonstrates the potential to provide a fast and efficient option for SV and speech assessment in patients after laryngeal oncosurgery. Furthermore, this approach determines the research gap in automated speech pathology detection concerning laryngeal cancer. In some circumstances, speech signals used with machine learning algorithms cannot ensure high accuracy or save time in pathology monitoring systems, limiting their practical application. This results in the need for research highlighting the essential concerns and challenges confronting vocal pathology detection systems and their practical application. To the best of our knowledge, there is very little evidence in the literature on the use of AI technology for SV evaluation [17; 18; 19; 20; 21]. As a result, using AI-based models for objective SV evaluation and categorization might pave the way for new research and clinical practice directions. Furthermore, a consistent computerized objective SV evaluation technique would enable comparisons of patient groups in different medical centers without the need for expert professional assistance. The same data sets might be utilized to refine the algorithm further. Several recent research articles have attempted to discriminate between normal and abnormal voices by employing a variety of machine learning-based classifiers capable of detecting diseased voices [14; 22; 23]. A broad range of statistical, machine learning-based, and other algorithms are currently available for detecting disordered voices based on computed acoustic aspects of the input signal [24]. Pathology categorization approaches typically fall under two categories: statistical and deep learning-based [25]. The statistical approach is frequently based on k-nearest neighbor, random forests, support vector machines, Gaussian mixture models, and others [26; 27; 28; 29; 30; 31; 32; 33].