# An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing
## 4 Discussion  
The proposed ASVI has the potential to be used in research and clinical practice as an easy-to-use metric for SV and speech changes in patients after laryngeal oncosurgery. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & \(n\) & **ASVI (SD)** & \(p\) \\ \hline Control (class 0) & 29 & 28.28 (2.93) & 0.001 \\ \hline Cordectomy and partial laryngectomy (class 1) & 29 & 15.39 (7.31) & 0.001 \\ \hline Total laryngectomy (class 2) & 12 & 8.48 (3.53) & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The mean acoustic substitution voicing index (ASVI) scores in control and patients’ groups. Figure 11: Scatterplot illustrating the correlation between the Impression, Intelligibility, Noise, Fluency, and Voicing perceptual rating scale (IINFVo) and the acoustic substitution voicing index (ASVI) with a 95% confidence interval. This index combines the benefits provided by contemporary AI-based machine learning and conventional acoustic speech assessment approaches. A statistically significant, strong correlation was revealed between the ASVI assessment results and the auditory-perceptual evaluation on the IINFVo scale provided by the trained physicians. As with any oncological condition, laryngeal cancer patients require continuous follow-up and ongoing care. This may be hindered by several socioeconomic factors, COVID-19 pandemic-related healthcare interruptions among them. After laryngeal oncosurgery, patients, especially those who live in rural areas, are often faced with limited availability of specialized medical care [68]. As a result, there are clear benefits to offering a new framework by employing telecommunication means and artificial intelligence approaches for automated speech analysis in the context of remotely-provided healthcare services [69]. Telehealth consultations have already been shown to be beneficial for various laryngeal pathologies, with diagnostic decision results equivalent to in-patient examination [70]. From a medical standpoint, the ASVI could potentially be useful in detecting and objectively measuring changes in SV. To begin with, this would help patients to seek treatment on time after laryngeal oncosurgery, as changes in voice and speech are usually the first sign of laryngeal cancer recurrence. Consequently, early diagnosis of the recurrence and subsequent intervention results in better 5-year survival rates in most cases. Furthermore, the ability to monitor patients' speech at home or in non-specialized patient facilities may lead to early detection of faulty tracheoesophageal prosthesis (TEP). Consequently, this would ensure the timely replacement of the TEP and avoid life-threatening complications, such as dislocation of the TEP and asphyviation [71]. While AI-based machine learning is already a mainstay in early diagnosis in cardiology and pulmonology, it becomes less common when it comes to the assessment of laryngeal pathology, namely SV, after laryngeal oncosurgery [72]. Currently, the AI-based research on this topic is hindered by the small sample sizes in different languages and researchers using different methodologies to evaluate speech, as noted in the systematic review conducted by [PERSON] et al. [73]. However, ASVI could be applied retrospectively to already-available speech datasets and provide a simple-to-interpret metric. Additionally, generated acoustic parameters provide a more in-depth look for researchers. The combination of AI-based machine learning and objective acoustic parameters provides a further benefit, as ASVI requires fewer resources for cross-cultural adaptation when compared to routine speech evaluation methods tools, i.e., special questionnaires. The proposed version of ASVI has the benefit of a lightweight algorithm. This benefits the user in two ways. The rapid response rate allows the SV analysis to be performed in real time instead of using pre-recorded speech samples. Additionally, this approach does not require high-end hardware to run efficiently.