Grapheme-based Automatic Speech Recognition using Probabilistic Lexical Modeling

@inproceedings{Harsha2014GraphemebasedAS,
  title={Grapheme-based Automatic Speech Recognition using Probabilistic Lexical Modeling},
  author={Jagan Harsha and P. Kavitha and Sriram Pranay and Sucheta and Gulcan Guillem and Gy{\"o}rgy Gw{\'e}nol{\'e} and Holger Hari and Ivana Hui and Joan M James and Lawrence Kenneth and Marc Leo and M. A. De Marco and Nesli Milos and Oya Novi and Petr Paco and Pierre-Edouard Phil and R. Palomo Pinto and Riwal Rapha{\"e}l and Rui Ronan and S. Assem Samira and S. D. Serena and T. Srikanth and T. Thomas},
  year={2014}
}
Automatic speech recognition (ASR) systems incorporate expert knowledge of language or the linguistic expertise through the use of phone pronunciation lexicon (or dictionary) where each word is associated with a sequence of phones. The creation of phone pronunciation lexicon for a new language or domain is costly as it requires linguistic expertise, and includes time and money. In this thesis, we focus on effective building of ASR systems in the absence of linguistic expertise for a new domain… CONTINUE READING

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