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Comparison of Rule-based and Statistical Methods for Grapheme to Phoneme Modelling
Ilze Auziņa, Mārcis Pinnis, Roberts Darǵis
57 - 60
10.3233/978-1-61499-442-8-57
Frontiers in Artificial Intelligence and Applications
Volume 268: Human Language Technologies – The Baltic Perspective
Abstract

Grapheme to phoneme modelling is one of the key features in automated speech recognition and speech synthesis. In this paper, the authors compare two different approaches: a statistical machine translation based method using the phonetically transcribed Latvian Speech Recognition Corpus and a rule-based method for phonetic transcription of words from grammatically correct forms. The paper provides 10-fold cross-validation results and error analysis for both methods.

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