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Unsupervised decomposition of words for speech recognition and retrieval
Mikko Kurimo, Teemu Hirsimäki, Ville Turunen, Sami Virpioja and Niklas Raatikainen
In: 13th International Conference Speech and Computer, SPECOM 2009(2009).
AbstractMany tasks in speech and language technology require a statis-
tical language model that covers a very large vocabulary. Such
tasks include automatic speech recognition, audio indexing and
retrieval and speech translation. A large vocabulary model is
typically needed to predict the probability of occurrence of
words and word sequences in different contexts. However, in
morphologically complex languages, the high level of word in-
ection, composition and derivation increases the amount of
different word forms so much that it becomes prohibitive to
to construct a sufcient vocabulary. Recently, several effective
methods have been developed to dene the suitable sub-word
units for building the models. To nd out which methods are
most effective in practice, we maintain an evaluation framework
called Morpho Challenge where different morphemes can be
compared in various state-of-the-art practical evaluation tasks.
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