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001 978-3-319-41607-6
003 DE-He213
005 20220801222445.0
007 cr nn 008mamaa
008 160829s2017 sz | s |||| 0|eng d
020 _a9783319416076
_9978-3-319-41607-6
024 7 _a10.1007/978-3-319-41607-6
_2doi
050 4 _aTK5102.9
072 7 _aTJF
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aTJF
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072 7 _aUYS
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082 0 4 _a621.382
_223
100 1 _aDonaj, Gregor.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_961595
245 1 0 _aLanguage Modeling for Automatic Speech Recognition of Inflective Languages
_h[electronic resource] :
_bAn Applications-Oriented Approach Using Lexical Data /
_cby Gregor Donaj, Zdravko Kačič.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aVIII, 71 p. 13 illus., 6 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,
_x2191-7388
505 0 _aIntroduction -- Speech Recognition in Inflective Languages -- Performance Evaluation Using Lexical Data -- Application Oriented Language Modeling -- An Example Application -- Conclusion.
520 _aThis book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages. Concentrates on speech recognition for inflective languages – representative of roughly half of Europe -- and their unique characteristics Introduces new application-oriented methods for measuring the performance of a speech recognition system Presents examples of language modeling to maximize the performance of a speech recognition system Provides techniques for analyzing errors and identifying their sources in a speech recognition system from a lexical point of view rather than acoustic point of view.
650 0 _aSignal processing.
_94052
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
700 1 _aKačič, Zdravko.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_961596
710 2 _aSpringerLink (Online service)
_961597
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319416052
776 0 8 _iPrinted edition:
_z9783319416069
830 0 _aSpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,
_x2191-7388
_961598
856 4 0 _uhttps://doi.org/10.1007/978-3-319-41607-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80789
_d80789