## 3 Normalization  
Text normalization refers to replacing non-standard spellings with their standard equivalents. For example, it replaces the English colloquial \"kinda\" with the standard  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Dataset** & **Segment** \\ \hline [PERSON] & lejte to se je spremenil \\  & glejte to se je spremenilo \\  & look this has changed \\ \hline [PERSON] & ja j@z j@z sn si vedno zapomnla kdaj sn mela izpit no \\  & ja jaz jazz sem si vedno zapomnila kdaj sem imela izpit no \\  & I always remembered when I had an exam. \\ \hline [PERSON]\_J & loh j@z se neki dodam \\  & lahko jaz se nekaj dodam \\  & can I add something else \\ \hline [PERSON]\_N & pa tuti f könčni fazi je ful velik totega piratstva ne \\  & pa tuti v könčni fazi je ful velik totega piratstva ne \\  & and in the end, there’s really a lot of this piracy, isn’t there \\ \hline [PERSON]\_P & zdej seveda je porociló priprauljeno \\  & zdaj seveda je porociló priprauljeno \\  & now, of course, the report is ready \\ \hline \hline \end{tabular}
\end{table}
Table 1: Normalization examples from different Slovenian datasets. The first row presents the literal form of transcribed speech, the second row is the standard form, and the third is the translation to English.  
form \"kind of\". It reduces the impact of unknown word forms appearing in non-standard language. In speech, normalization establishes connections between different spoken variants of the same word or expressions of the same meaning. It is mainly defined as one-to-one or one-to-many mapping with fixed word order. Some characteristics of spoken language lie outside the scope of this definition. Examples are hesitations, false starts, and self-corrections. What to do with this phenomenon depends on the NLP task for which the normalization is used. In our research, we use the following definition of the normalization [2]: Lexical normalization is the task of transforming a literal transcription of an utterance into its standard form, word-by-word, including one-to-one, one-to-many, and many-to-one replacements. Deletions and insertions of complete words are beyond the scope of this definition. Lexical normalization is, to some extent, a subjective task, as, in some cases, multiple annotations are plausible. Furthermore, in certain circumstances, annotators disagree on whether normalization is necessary for specific words.  
Text normalization is beneficial in various NLP tasks, e.g., information retrieval, machine translation, sentiment analysis, etc. It unifies the language or at least increases its homogeneity by handling language variations present in user-generated text in social media, historical texts, and speech, where dialectal expressions also take place. A prerequisite for performing text normalization automatically is the availability of datasets with normalization examples that are sufficiently large. In our research, we limit the experiments to speech.  
The term text normalization could also refer to the process of verbalizing numbers and short representations (e.g., converting something like \"3 lb\" into its verbalization \"three pounds\"), for example, as part of a text-to-speech system [3,4]. This type of normalization is not the topic of this paper.