Using Morphological Data in Language Modeling for Serbian Large Vocabulary Speech Recognition.

Clicks: 357
ID: 49854
2019
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Abstract
Serbian is in a group of highly inflective and morphologically rich languages that use a lot of different word suffixes to express different grammatical, syntactic, or semantic features. This kind of behaviour usually produces a lot of recognition errors, especially in large vocabulary systems-even when, due to good acoustical matching, the correct lemma is predicted by the automatic speech recognition system, often a wrong word ending occurs, which is nevertheless counted as an error. This effect is larger for contexts not present in the language model training corpus. In this manuscript, an approach which takes into account different morphological categories of words for language modeling is examined, and the benefits in terms of word error rates and perplexities are presented. These categories include word type, word case, grammatical number, and gender, and they were all assigned to words in the system vocabulary, where applicable. These additional word features helped to produce significant improvements in relation to the baseline system, both for n-gram-based and neural network-based language models. The proposed system can help overcome a lot of tedious errors in a large vocabulary system, for example, for dictation, both for Serbian and for other languages with similar characteristics.
Reference Key
pakoci2019usingcomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pakoci, Edvin;Popović, Branislav;Pekar, Darko;
Journal Computational Intelligence and Neuroscience
Year 2019
DOI
10.1155/2019/5072918
URL
Keywords
Machine learning text mining medline gold standard author name disambiguation

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