Language Modeling for Automatic Speech Recognition of Inflective Languages An Applications-Oriented Approach Using Lexical Data /

• Κύριοι συγγραφείς: Donaj, Gregor (Συγγραφέας), Kačič, Zdravko (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: SpringerBriefs in Electrical and Computer Engineering,
• Θέματα: Engineering.; Computational linguistics.; Signal, Image and Speech Processing.; Language Translation and Linguistics.; Computational Linguistics.
• Φυσική περιγραφή: VIII, 71 p. 13 illus., 6 illus. in color. online resource.
• ISBN: 9783319416076
• ISSN: 2191-8112

This 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.