A data-driven method for modeling pronunciation variation

@article{Kessens2003ADM,
  title={A data-driven method for modeling pronunciation variation},
  author={Judith M. Kessens and Catia Cucchiarini and Helmer Strik},
  journal={Speech Communication},
  year={2003},
  volume={40},
  pages={517-534}
}
This paper describes a rule-based data-driven (DD) method to model pronunciation variation in automatic speech recognition (ASR). The DD method consists of the following steps. First, the possible pronunciation variants are generated by making each phone in the canonical transcription of the word optional. Next, forced recognition is performed in order to determine which variant best matches the acoustic signal. Finally, the rules are derived by aligning the best matching variant with the… CONTINUE READING
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