Paper Info
Title
Using Morphological Data in Language Modeling for Serbian Large Vocabulary Speech Recognition.
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 systemseven 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.
Year
DOI
Venue
2019
10.1155/2019/5072918
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
Field
DocType
Volume
Grammatical number,Serbian,Computer science,Dictation,Natural language processing,Artificial intelligence,Artificial neural network,Vocabulary,Syntax,Language model,Machine learning,Lemma (mathematics)
Journal
2019
ISSN
Citations 
PageRank 
1687-5265
1
0.36
References 
Authors
1
3
Name
Order
Citations
PageRank
Edvin Pakoci1103.02
Branislav M. Popovic29617.13
Darko Pekar3378.28