# An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing
## 1 Introduction  
### Issue  
Laryngeal carcinoma is one of the rare oncological illnesses with a declining 5-year survival rate over the last several decades. In the last 40 years, it has fallen from 66 to 63 percent. Some researchers ascribe this considerable decline to better organ-preserving treatment procedures and circumstances that may affect the patient's follow-up, most notably a lack of specialist medical care near the patient's domicile [1]. Meanwhile, the American Cancer Society predicts that there will be 12,470 new cases and 3820 fatalities from laryngeal cancer in the United States alone in 2022 [2]. The true impact of the COVID-19 pandemic on the 5-year survival rate and quality of life of these patients remains unknown. However, allocating specialized medical facilities and personnel to contain the recent COVID-19 pandemic led to delayed diagnostics and treatment of both primary and recurrent laryngeal cancer [3, 4]. Due to the fact that more than half of laryngeal cancer patients arrived with stage III or IV at their initial consultation, this necessitated more aggressive laryngeal cancer therapies and increased patient mortality [1]. Laryngeal preserving surgery, complete removal of the larynx, chemotherapy, radiotherapy, or a combination of these methods are usually used to achieve remission [5]. A complete removal of the larynx may also be necessary if chemotherapy and radiotherapyare ineffective or unavailable. Following laryngeal oncosurgery treatments, these individuals are often limited to using only a single vocal fold oscillating with the remaining laryngeal and pharyngeal tissues for phonation. After total laryngectomy, the only way to communicate is by alaryngeal (esophageal or tracheoesophageal) speaking. This causes varying degrees of speech impairment or even total inability to speak. Substitution voicing (SV) is the situation that occurs following laryngeal oncosurgery when voicing is achieved without one or two vocal folds [6]. The body begins to use the remaining structures (ventricular folds, arvepigolitotic folds, pharyngeal mucosa), which were not previously used, to substitute the missing laryngeal structures in closing the phonatory gap and producing voice. The patient retains the ability to phonate and speak, but previously unintended anatomical structures substitute voice production. The downside of this phenomenon is that both SV and speech following laryngeal oncosurgery exhibit significant irregularity, frequency changes, aperiodicity, and phonatory interruptions [7]. With traditional approaches, these characteristics render SV assessment potentially incorrect, if not impossible [8; 9]. Voice loss substantially impacts the quality of life of laryngeal cancer patients, significantly affecting their social life and well-being [10]. Furthermore, because impaired communication abilities cause discomfort, 40-57 percent of these people develop depression [10]. This can be alleviated in part by postsurgical voice and speech rehabilitation. Despite their effectiveness, all recognized speech rehabilitation procedures produce distinctively altered patient speech patterns. These discrepancies are particularly obvious when the patient is compelled to speak in a loud setting or on the phone [11]. Additionally, patients who were unable to use a phone independently during the COVID-19 epidemic had to rely on text messaging to communicate with their relatives and found it challenging to get basic social or telemedicine care. Healthcare providers frequently rely on specialists' opinions on perceived speech quality, disability classification, and pathology diagnosis. This method is frequently time-consuming and prone to parameter sensitivity [12]. Computer-assisted medical approaches have grown in prominence over the last few decades. According to conventional wisdom, the acoustic prosodic qualities of a speech signal can be altered by a range of health-related events [13].