Transducer Composition for Context-Dependent Network Expansion

Michael Riley, Fernando Pereira, Mehryar Mohri

Research output: Contribution to conferencePaperpeer-review


Context-dependent models for language units are essential in high-accuracy speech recognition. However, standard speech recognition frameworks are based on the substitution of lower-level models for higher-level units. Since substitution cannot express context-dependency constraints, actual recognizers use restrictive model-structure assumptions and specialized code for context-dependent models, leading to decreased flexibility and lost opportunities for automatic model optimization. Instead, we propose a recognition framework that builds in the possibility of context dependency from the start by using weighted finite-state transduction rather than substitution. The framework is implemented with a general demand-driven transducer composition algorithm that allows great flexibility in model structure, form of context dependency and network expansion method, while achieving competitive recognition performance.

Original languageEnglish (US)
Number of pages4
StatePublished - 1997
Event5th European Conference on Speech Communication and Technology, EUROSPEECH 1997 - Rhodes, Greece
Duration: Sep 22 1997Sep 25 1997


Conference5th European Conference on Speech Communication and Technology, EUROSPEECH 1997

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Linguistics and Language
  • Communication


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