Statistical and Linguistic Clustering for Language Modeling in ASR

  • R. Justo
  • I. Torres
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

In this work several sets of categories obtained by a statistical clustering algorithm, as well as a linguistic set, were used to design category-based language models. The language models proposed were evaluated, as usual, in terms of perplexity of the text corpus. Then they were integrated into an ASR system and also evaluated in terms of system performance. It can be seen that category-based language models can perform better, also in terms of WER, when categories are obtained through statistical models instead of using linguistic techniques. They also show that better system performance are obtained when the language model interpolates category based and word based models.

Keywords

Language Model Statistical Cluster Training Corpus Text Corpus Speech Recognition System 
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 2005

Authors and Affiliations

  • R. Justo
    • 1
  • I. Torres
    • 1
  1. 1.Departamento de Electricidad y Electrónica, Facultad de Ciencia y TecnologíaUniversidad del País Vasco 

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