Central Library

Normal view MARC view ISBD view

Language Modeling for Automatic Speech Recognition of Inflective Languages [electronic resource] : An Applications-Oriented Approach Using Lexical Data / by Gregor Donaj, Zdravko Kačič.

By: Donaj, Gregor [author.].
Contributor(s): Kačič, Zdravko [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: VIII, 71 p. 13 illus., 6 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319416076.Subject(s): Signal processing | Natural language processing (Computer science) | Computational linguistics | Signal, Speech and Image Processing | Natural Language Processing (NLP) | Computational LinguisticsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
Introduction -- Speech Recognition in Inflective Languages -- Performance Evaluation Using Lexical Data -- Application Oriented Language Modeling -- An Example Application -- Conclusion.
In: Springer Nature eBookSummary: 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.
    average rating: 0.0 (0 votes)
No physical items for this record

Introduction -- Speech Recognition in Inflective Languages -- Performance Evaluation Using Lexical Data -- Application Oriented Language Modeling -- An Example Application -- Conclusion.

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.

There are no comments for this item.

Log in to your account to post a comment.