Normalization of Non-Standard Words in Croatian Texts

TL;DR

This paper introduces a rule-based text normalization approach for Croatian, expanding non-standard words with high accuracy, crucial for improving text-to-speech systems in the language.

Contribution

It proposes a comprehensive taxonomy and normalization methods combining rules and lookup dictionaries tailored for Croatian language.

Findings

Token normalization rate of 95% achieved.

80% of expanded words are morphologically correct.

Method enhances text-to-speech system accuracy.

Abstract

This paper presents text normalization which is an integral part of any text-to-speech synthesis system. Text normalization is a set of methods with a task to write non-standard words, like numbers, dates, times, abbreviations, acronyms and the most common symbols, in their full expanded form are presented. The whole taxonomy for classification of non-standard words in Croatian language together with rule-based normalization methods combined with a lookup dictionary are proposed. Achieved token rate for normalization of Croatian texts is 95%, where 80% of expanded words are in correct morphological form.