Voice Recognition White Papers
Vocabulary Decomposition for Estonian Open Vocabulary Speech Recognition
Overview Speech recognition in many morphologically rich languages suffers from a very high Out-Of-Vocabulary (OOV) ratio. Earlier work has shown that vocabulary decomposition methods can practically solve this problem for a subset of these languages. This paper compares various vocabulary decomposition approaches to open vocabulary speech recognition, using Estonian speech recognition as a benchmark. Comparisons are performed utilizing large models of 60000 lexical items and smaller vocabularies of 5000 items. A large vocabulary model based on a manually constructed morphological tagger is shown to give the lowest word error rate, while the unsupervised morphology discovery method Morfessor Baseline gives marginally weaker results. Only the Morfessor-based approach is shown to adequately scale to smaller vocabulary sizes.
| Publisher | Helsinki University of Technology | File Format | |
|---|---|---|---|
| Date Published | May 2007 | ||
| Format | White Papers | ||
| Topics | |||
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