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

ISBN-13: 9783319416052 / Angielski / Miękka / 2016 / 71 str.

Gregor Donaj; Zdravko Kacic

Opis

This book covers language modeling and automatic speech recognition for inflective languages (e.g. 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.

Spis treści

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

Blurb

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