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.
This work has been partially supported by the CICYT proyect TIC2002-04103-C03-02 and by the Universidad del País Vasco under grant 9/UPV 00224.310-13566/2001.
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Justo, R., Torres, I. (2005). Statistical and Linguistic Clustering for Language Modeling in ASR. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_58
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DOI: https://doi.org/10.1007/11578079_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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