## 4 Results
The application was implemented in the C++ programming language. The implemented method uses a dictionary of 5018 words and 767 defined conversion rules. For comparison, the software presented by the author in this paper was implemented in Python programming language, 975 conversion rules were implemented and the dictionary is very limited and plays only a supporting role. This means that TransFon has implemented 208 more transcription rules, which is over 27% more. The application failed to compile due to the lack of inclusion in the source code of the appropriate libraries that were used by the programmer to create the application. This made it impossible to evaluate the correctness of the application and seriously hindered the comparison with the software created by the author of the paper; However, based on the analysis of the application's source code, you can see that the principle of the application is also rule-based, but the author of the Transcriber application tried to refine and improve the application's performance by adding new words to the dictionary (exceptions). The author of the TransFon application, on the other hand, tried to add and supplement transcription rules in a similar way as is known in the literature. This is evidenced by the dictionary size used in both applications;
* The G2P system presented here could be used for Polish corpus development;
* The G2P implementation presented here did not exploit any similar pre-existing tools [48];
* It is worth noting that the solutions presented here for the development of language and speech corpora in Polish are not the only ones and publications on this subject are available [72, 73];
* Of particular interest are the results presented in publications by [PERSON] et al. [62, 63, 64, 39, 65, 66, 37]. This paper presents a rule-based method for grapheme-to-phoneme conversion and an implementation for Polish. The major original author's achievements presented in this paper are as follows:

* Implementation of the known from the linguistic literature rules of converting graphemes into phonemes for the Polish language in the Python programming language [36, 37, 40, 44];
* Developing an algorithm for automatic conversion of graphemes into phonemes for the Polish language and implementing it in the Python programming language with numerous improvements;
* Development of a software for automatic conversion of graphemes into phonemes called TransFon, which enables automatic conversion of graphemes into phonemes of any orthographic text files in the Polish language;
* Application of the developed methods to create phoneme-based language corpora using the automatic conversion of graphemes to phonemes;
* Statistical analysis of the occurrence frequency of particular grapheme-to-phoneme conversion rules in Polish;
* Comparison of the results obtained with those published in the literature and discussion. It should be noted that the research presented in this article used basic principles and the fundamental grapheme-to-phoneme conversion rules developed by [PERSON] and presented in her set of monographs dedicated to the automatic grapheme-to-phoneme conversion of texts in Polish [36, 37]. The author used previously developed rules and developed independently the grapheme-to-phoneme conversion algorithm and softwareapplication. The application allows to convert any text in Polish orthography to the corresponding strings of phonemes as well as creating large phonemic language corpora based on orthographic language corpora. The system for rule-based grapheme-to-phoneme conversion implemented here is complemented by dictionary-based methods, and was used to obtain statistics for the use of grapheme-to-phoneme conversion rules in Polish, potentially enabling the improvement of grapheme-to-phoneme conversion for Polish in the future. The grapheme-to-phoneme conversion system developed and its ability to create phonemic language corpora for Polish open up further opportunities for research on improving automatic speech recognition in Polish.