Comparison of Rule-based and Statistical Methods for Grapheme to Phoneme Modelling
Auzi ncedil a Ilze, Pinnis M amacr rcis, Roberts Darģis · Frontiers in artificial intelligence and applications · 2014
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.