A Rule-Based Grapheme-to-Phoneme Conversion System

Abstract

This article presents a rule-based grapheme-to-phoneme conversion method and algorithm for Polish. It should be noted that the fundamental grapheme-to-phoneme conversion rules have been developed by Maria Steffen-Batóg and presented in her set of monographs dedicated to the automatic grapheme-to-phoneme conversion of texts in Polish. The author used previously developed rules and independently developed the grapheme-to-phoneme conversion algorithm.The algorithm has been implemented as a software application called TransFon, which allows the user to convert any text in Polish orthography to corresponding strings of phonemes, in phonemic transcription. Using TransFon, a phonemic Polish language corpus was created out of an orthographic corpus. The phonemic language corpusallows statistical analysis of the Polish language, as well as the development of phoneme- and word-based language models for automatic speech recognition using statistical methods. The developed phonemic language corpus opens up further opportunities for research to improve automatic speech recognition in Polish. The development of statistical methods for speech recognition and language modelling requires access to large language corpora, including phonemic corpora. The method presented here enables the creation of such corpora.

WITHDRAWN: Integrating Prior Knowledge and Data-Driven Approaches for Improving Grapheme-to-Phoneme Conversion in Korean Language

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Text-To-Speech in Voice Assistants: Challenges and Mitigation Strategies

“…G2P systems allow users to enforce the desired pronunciation by providing a phonetic transcript of the input. Instead of models, rule based G2P conversion is another alternative, provided linguistic specialists have created these conversion rules [18].…”

Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State Transducers

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“…Word processing speed (WPS) is one indicator of a g2p algorithm's effectiveness [13]. The WPS for the applications, Unified Parser [4], Espeak 7 and the proposed tool Mlphon was estimated by measuring the time required to convert the 100k common words in Malayalam listed in Indic NLP corpus [19] as per (2).…”