Processing Self-Corrections in a Speech-to-Speech System

  • Jörg Spilker
  • Martin Klarner
  • Günther Görz
Part of the Artificial Intelligence book series (AI)


Self-repairs are a frequent phenomenon in spontaneous speech. The ability to detect and correct those repairs is therefore indispensable for any spoken language system. We present a framework for detection and correction of speech repairs where all relevant levels of information, i.e., acoustics, lexis, syntax and semantics can be integrated. The basic idea is to reduce the search space for repairs as soon as possible by cascading filters that involve more and more features. At first an acoustic module generates hypotheses about the existence of a repair. Afterwards a stochastic model suggests a correction for every hypothesis. Hihgly scored corrections are inserted as new paths in the word lattice. Finally, a lattice parser decides wether the repair should be accepted or not.


Interruption Point Statistical Machine Translation Spontaneous Speech Word Fragment Speech Recognizer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Jörg Spilker
    • 1
  • Martin Klarner
    • 1
  • Günther Görz
    • 1
  1. 1.Department of Computer ScienceUniversität Erlangen-NürnbergGermany

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