Vesa Siivola

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We explore the use of morph-based language models in large-vocabulary continuous-speech recognition systems across four so-called morphologically rich languages: Finnish, Estonian, Turkish, and Egyptian Colloquial Arabic. The morphs are subword units discovered in an unsupervised, data-driven way using the <i>Morfessor</i> algorithm. By estimating(More)
In the speech recognition of highly inflecting or compounding languages, the traditional word-based language modeling is problematic. As the number of distinct word forms can grow very large, it becomes difficult to train language models that are both effective and cover the words of the language well. In the literature, several methods have been proposed(More)
We study continuous speech recognition based on sub-word units found in an unsupervised fashion. For agglutinative languages like Finnish, traditional word-based n-gram language modeling does not work well due to the huge number of different word forms. We use a method based on the Minimum Description Length principle to split words statistically into(More)
It is practically impossible to build a word-based lexicon for speech recognition in agglutinative languages that would cover all the relevant words. The problem is that words are generally built by concatenating several prefixes and suffixes to the word roots. Together with compounding and inflections this leads to millions of different, but still frequent(More)
Traditionally, when building an n-gram model, we decide the span of the model history, collect the relevant statistics and estimate the model. The model can be pruned down to a smaller size by manipulating the statistics or the estimated model. This paper shows how an n-gram model can be built by adding suitable sets of n-grams to a unigram model until(More)
We analyze subword-based language models (LMs) in large-vocabulary continuous speech recognition across four “morphologically rich” languages: Finnish, Estonian, Turkish, and Egyptian Colloquial Arabic. By estimating n-gram LMs over sequences of morphs instead of words, better vocabulary coverage and reduced data sparsity is obtained. Standard word LMs(More)
-gram models are the most widely used language models in large vocabulary continuous speech recognition. Since the size of the model grows rapidly with respect to the model order and available training data, many methods have been proposed for pruning the least relevant -grams from the model. However, correct smoothing of the -gram probability distributions(More)
Statistical language modeling (SLM) is an essential part in any large-vocabulary continuous speech recognition (LVCSR) system. The development of the standard SLM methods has been strongly affected by the goals of LVCSR in English. The structure of Finnish is substantially different from English, so if the standard SLMs are directly applied, the success is(More)
Language modelling in large vocabulary speech recognition has traditionally been based on words. A lexicon of the most common words of the language in question is created and the recogniser is limited to consider only the words in the lexicon. In Finnish, however, it is more difficult to create an extensive lexicon, since the compounding of words, numerous(More)