## 4 Results
The \(R^{2}\) value here is equal to 0.97509 and indicates that [PERSON]'s equation fits very well the obtained statistical data on orthographic letter frequencies in Polish. Analogous regularity was observed for the frequency distribution of the grapheme-to-phoneme conversion rules for Polish presented in Table 22. Figure 5 presents the fit of [PERSON]'s equation to the ranked frequency distribution of grapheme-to-phoneme conversion rules used for Polish. The summary of evaluation results for the fit of [PERSON]'s equation to the ranked frequency distribution of orthographic letters (1) and grapheme-to-phoneme conversion rules in Polish (2) is presented in Table 24. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **[PERSON]’s Equation** & \(\mathbf{R^{2}}\) & \(RMSE\) \\ \hline
1 & \(Y_{r}=\frac{0.06753}{r^{-0.08}}\cdot 0.91^{r}\) & 0.97509 & \(2.9071\cdot 10^{-3}\) \\ \hline
2 & \(Y_{r}=\frac{0.12512}{r^{2.08}}\cdot 0.954^{r}\) & 0.95107 & \(1.8022\cdot 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 24: The evaluation results of the fit of [PERSON]’s equation to the ranked frequency distribution of orthographic letters (1) and grapheme-to-phoneme conversion rules (2) in Polish. The values of \(R^{2}\) presented in Table 24 indicate that [PERSON]'s equation fits the obtained statistical data for the frequencies of orthographic letters and the grapheme-to-phoneme conversion rules in Polish. On this basis, it can be concluded that the data obtained from statistical analysis of grapheme-to-phoneme conversion rules used in Polish, based on an orthographic language corpus, are reliable. The results presented, from the statistical analysis of grapheme-to-phoneme conversion rules in Polish, represent the basis for a statistical approach to Polish G2P in a future implementation. ## 6 Conclusions

The results of the grapheme-to-phoneme conversion research presented in this paper were compared to other results published in the literature [3; 27; 39; 41; 42; 43; 45; 51; 53; 55; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73]. On the basis of this comparison, the following conclusions can be drawn:

* Automatic conversion of graphemes into phonemes in orthographic texts is not only a technical issue, consisting in developing appropriate algorithms for converting graphemes into phonemes, but also a serious linguistic problem. Only specialists in linguistics and phonetics of a given language are able to formulate appropriate rules for converting graphemes into phonemes for speech [51];
* An additional complication is that automatic conversion of graphemes to phonemes is a language-specific problem with different spelling and pronunciation conventions within the same language [55; 68; 69; 70];
* Effective solutions for automatic grapheme-to-phoneme conversion in one language may not help solve the same problems for a different language. There is not only one language and technical problem of automatic conversion of graphemes to phonemes to be solved, but many different problems with different levels of difficulty that should be solved for each language separately [51];

Figure 5: The fit of [PERSON]’s equation to the ranked frequency distribution of grapheme-to-phoneme conversion rules used for Polish. * Automatic grapheme-to-phoneme conversion is widely used not only in speech synthesis, but also in speech recognition [3, 53];
* A separate, but very important problem is the evaluation of grapheme-to-phoneme conversion processes [53, 71]. Evaluation and validation of grapheme-to-phoneme conversion implementations is a laborious and time-consuming process. All problems registered for the G2P implementation discussed in this paper were positively resolved;
* The G2P implementation developed for this research is not the only one for Polish [27, 39, 41, 43, 45], however only one of the others is available for free use [41];
* The author of the paper analysed for comparison the only available application for the Polish language, named Transcriber [41].