Lattice kernels for spoken-dialog classification

Corinna Cortes, Patrick Haffner, Mehryar Mohri

Research output: Contribution to journalConference articlepeer-review


Classification is a key task in spoken-dialog systems. The response of a spoken-dialog system is often guided by the category assigned to the speaker's utterance. Unfortunately, classifiers based on the one-best transcription of the speech utterances are not satisfactory because of the high word error rate of conversational speech recognition systems. Since the correct transcription may not be the highest ranking one, but often will be represented in the word lattices output by the recognizer, the classification accuracy can be much higher if the full lattice is exploited both during training and classification. In this paper we present the first principled approach for classification based on full lattices. For this purpose, we use the Support Vector Machine framework with kernels for lattices. The lattice kernels we define belong to the general class of rational kernels. We give efficient algorithms for computing kernels for arbitrary lattices and report experiments using the algorithm in a difficult call-classification task with 38 categories. Our experiments with a trigram lattice kernel show a 15% reduction in error rate at a 30% rejection level.

Original languageEnglish (US)
Pages (from-to)628-631
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 2003
Event2003 IEEE International Conference on Accoustics, Speech, and Signal Processing - Hong Kong, Hong Kong
Duration: Apr 6 2003Apr 10 2003

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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