Speech Recognition with Weighted Finite-State Transducers

This chapter describes a general representation and algorithmic framework for speech recognition based on weighted finite-state transducers

Mehryar Mohri

2007

Scholarcy highlights

  • This chapter describes a general representation and algorithmic framework for speech recognition based on weighted finite-state transducers
  • General algorithms for building and optimizing transducer models are presented, including composition for combining models, weighted determinization and minimization for optimizing time and space requirements, and a weight pushing algorithm for redistributing transition weights optimally for speech recognition. The application of these methods to large-vocabulary recognition tasks is explained in detail, and experimental results are given, in particular for the North American Business News task, in which these methods were used to combine hidden Markov models, full cross-word triphones, a lexicon of 40000 words, and a large trigram grammar into a single weighted transducer that is only somewhat larger than the trigram word grammar and that runs NAB in real time on a very simple decoder
  • Another example demonstrates that the same methods can be used to optimize lattices for second-pass recognition

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