## 6 Description of Test Data
A _segment_ in which all amplitude values are below the third part of the average amplitude value of the entire file are considered \"silence\". The minimum value of the pause length is taken as 0.1 s. The pause time is determined by the following formula: pause time = (frame length)/(sample rate),where length is the difference between the end and beginning of the current pause. 3. Clarity of speech. For speech recognition, we used the library <<CMUSphinx>>, which accepts an audio file as input [30]. The result of processing the file is a list of words. The recognition process is long and not always successful, as different people have different recording quality access and degrees of vocal intelligibility. To assess the intelligibility of speech, the developed test \"Read the text\" was used. As a result of passing the test, after the user read the specified text on the phone screen, the text that was pronounced by the phone user was recorded and recognized. The text was selected by doctors and psychologists. Each text was selected at random from a data set of 20 texts. The percentage of similarity between these texts was used as the measure of intelligibility. The original and recognized texts were compared using the Shingle algorithm, which includes: * The canonization of the text (i.e., removing all prepositions and symbols from the text); * Splitting the text into shingles (i.e., parts of the text selected for comparison), with a certain number of words in its sequence to check for uniqueness. The shingle size was taken as equal to two; * The calculation of shingle hashes using 84 static functions. 4. Speech rate. To determine the value of the rate of speech, it is necessary to divide the number of all spoken words by the duration of the entire audio file. The total number of words is known from the text obtained as a result of recognition. The duration of the entire audio file is defined as duration (in seconds) = (frame length)/(sample rate). 5. List of repeated words and their number of times in the text. Another characteristic feature of speech is the repetition of words or the pronunciation of words of filler words; for example, \"e\" and \"em,\" among others. Therefore, it is necessary to highlight these words and the number of repetitions of each of them. Examples of average voice metrics for two users are shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & **Average Pause** & **Maximum** & **Number of** & **Audibility (\%)** & **Number of Words** & **Popular Word** \\
**Number** & **Time (sec)** & **Pause Time** & **Pauses** & **Per Second** & **Popular Word** \\ \hline
1 & 0.29 & 0.44 & 6.8 & 34.73 & 1.74 & “and- -2 \\
2 & 0.28 & 0.56 & 6.25 & 16.67 & 1.92 & “ more =-2 \\ \hline
2 & 0.31 & 0.54 & 5.5 & 37.5 & 1.99 & - \\
2 & 0.27 & 0.47 & 5.5 & 31.02 & 1.99 & - \\ \hline
3 & 0.31 & 0.50 & 6.4 & 7.28 & 1.78 & - \\
3 & 0.22 & 0.48 & 5.5 & 5.79 & 1.63 & “ not, and -2 \\ \hline
4 & 0.26 & 0.36 & 4.65 & 28.35 & 1.70 & \\
4 & 0.18 & 0.27 & 3.5 & 8 & 1.74 & - \\ \hline
5 & 0.25 & 0.38 & 8 & 18.71 & 1.92 & - \\
5 & 0.27 & 0.48 & 5.5 & 4.16 & 1.91 & “in -2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average indicators of user voices. Table 2 shows that the number of pauses and the average pause time were very similar between users. This was a consequence of the fact that users read the same text. However, it is possible to notice a difference in the maximum length of the pause, as each person has their own intonation, which is different from that of the other. It can also be seen that the speech speed of both users was quite high, but the difference in some tests was noticeable. One of the most important metrics is intelligibility. From Table 2, it can be seen that intelligibility also depended on the text but, at the same time, it differed significantly for each of the users separately. This can be affected by various factors, such as the fast pronunciation of names and other words, or poor recording quality.