## 9 Conclusions and Future Work  
In this paper, we evaluate three different sequence-to-sequence models applied to the normalization of literal speech transcriptions for the highly inflected Slovenian language. In our experiments, character-based statistical models were taken as the baseline. The results show that models based on neural networks performed better. Generally, it is difficult to say which architecture is better, LSTM or transformer. Using established evaluation metrics for approximately half of the datasets, LSTM performed better, while for the other half, the transformer did. In some cases, WER and CER also led to different conclusions, as in inflected languages, often only one or two characters are incorrect in a  
Figure 3: Example HTML output with inserted word error (W-ins) and deleted word error (W-Del). \begin{table}
\begin{tabular}{l l} \hline \hline
**Form** & **Segment** \\ \hline Literal & To mi od takrat veilk pomen. \\ Standard & To mi od takrat veilko pomeni. \\ English & Since then, this means a lot to me. \\ \hline Literal & To ima od takrat veilk pomen. \\ Standard & To ima od takrat veilk pomen. \\ English & Since then, this has a great importance. \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Example segments with longer context needed for normalization. Figure 2: Example HTML output with deleted word error (W-del) due to a deleted space (missing conversion marked in red, unwarranted conversion marked in blue, and missing word marked in bright red). transformed word. This observation is particularly important if the final standard form is obtained through subsequent manual correction. Since established evaluation metrics do not give more detailed insight into the errors occurring during normalization, we developed a specialized tool to perform these tasks. A deeper inspection of errors reveals that, in the case of a transformer, less manual labor is needed to correct mistakes. We also added statistical tests to our tool. In our experiments, all but one system comparison indicated statistical significance. This test can be used in further research to indicate whether better results are due to system improvement or chance. Experiments were performed using three different basic units: characters, words, and subwords. Surprisingly, subword-based models were not better than character-based models. Both of them were used to deal with the problem of out-of-vocabulary words. In the literature, subword-based models usually outperform character-based models in many domains because they can use longer context. It seems that normalization does not require a longer context. Based on the conclusions of our research in this article, future work will go in two directions. We will explore how to take more control over subword splitting to make it specifically designed for normalization. In this research, we limited word-splitting to the unigram method. For further research, we aim to compare several word-splitting methods in this task. However, most of those methods have been developed to optimize some kind of cost function on a training set given in only one form. We aim to define a word-splitting method specifically tailored to capture typical characteristics of spoken language and with a cost function that would be defined on two text forms (literal and standard) and their alignment. The second direction for future work is aimed at the data scarcity problem. Limited datasets will be expanded using data augmentation techniques such as back-translation. Conceptualization, M.S.M. and D.V. ; methodology, G.D. and M.S.M. ; software, G.D.; formal analysis, G.D. and M.S.M. ; writing, M.S.M., D.V. and G.D.; funding acquisition, D.V. All authors have read and agreed to the published version of the manuscript. The research presented in this paper was conducted within the research project titled Basic Research for the Development of Spoken Language Resources and Speech Technologies for the Slovenian Language (J7-4642) funded by the Slovenian Research and Innovation Agency. Not applicable.